From c721884cf5b9692c32461ffdecfc9121ca0d47b4 Mon Sep 17 00:00:00 2001 From: AUTOMATIC1111 <16777216c@gmail.com> Date: Tue, 8 Aug 2023 18:32:18 +0300 Subject: Split history: mv temp modules/sd_samplers_kdiffusion.py --- modules/sd_samplers_kdiffusion.py | 511 ++++++++++++++++++++++++++++++++++++++ 1 file changed, 511 insertions(+) create mode 100644 modules/sd_samplers_kdiffusion.py (limited to 'modules/sd_samplers_kdiffusion.py') diff --git a/modules/sd_samplers_kdiffusion.py b/modules/sd_samplers_kdiffusion.py new file mode 100644 index 00000000..db71a549 --- /dev/null +++ b/modules/sd_samplers_kdiffusion.py @@ -0,0 +1,511 @@ +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.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): + return torch.cat(conds) + + return {key: torch.cat([x[key] for x in conds]) for key in conds[0].keys()} + + +def subscript_cond(cond, a, b): + if not isinstance(cond, dict): + return cond[a:b] + + return {key: vec[a:b] for key, vec in cond.items()} + + +def pad_cond(tensor, repeats, empty): + if not isinstance(tensor, dict): + return torch.cat([tensor, empty.repeat((tensor.shape[0], repeats, 1))], axis=1) + + tensor['crossattn'] = pad_cond(tensor['crossattn'], repeats, empty) + return tensor + + +class CFGDenoiser(torch.nn.Module): + """ + Classifier free guidance denoiser. A wrapper for stable diffusion model (specifically for unet) + that can take a noisy picture and produce a noise-free picture using two guidances (prompts) + instead of one. Originally, the second prompt is just an empty string, but we use non-empty + negative prompt. + """ + + def __init__(self, model): + super().__init__() + self.inner_model = model + self.mask = None + self.nmask = None + self.init_latent = None + self.step = 0 + self.image_cfg_scale = None + self.padded_cond_uncond = False + + def combine_denoised(self, x_out, conds_list, uncond, cond_scale): + denoised_uncond = x_out[-uncond.shape[0]:] + denoised = torch.clone(denoised_uncond) + + for i, conds in enumerate(conds_list): + for cond_index, weight in conds: + denoised[i] += (x_out[cond_index] - denoised_uncond[i]) * (weight * cond_scale) + + return denoised + + def combine_denoised_for_edit_model(self, x_out, cond_scale): + out_cond, out_img_cond, out_uncond = x_out.chunk(3) + denoised = out_uncond + cond_scale * (out_cond - out_img_cond) + self.image_cfg_scale * (out_img_cond - out_uncond) + + return denoised + + def forward(self, x, sigma, uncond, cond, cond_scale, s_min_uncond, image_cond): + if state.interrupted or state.skipped: + raise sd_samplers_common.InterruptedException + + # at self.image_cfg_scale == 1.0 produced results for edit model are the same as with normal sampling, + # so is_edit_model is set to False to support AND composition. + is_edit_model = shared.sd_model.cond_stage_key == "edit" and self.image_cfg_scale is not None and self.image_cfg_scale != 1.0 + + conds_list, tensor = prompt_parser.reconstruct_multicond_batch(cond, self.step) + uncond = prompt_parser.reconstruct_cond_batch(uncond, self.step) + + assert not is_edit_model or all(len(conds) == 1 for conds in conds_list), "AND is not supported for InstructPix2Pix checkpoint (unless using Image CFG scale = 1.0)" + + batch_size = len(conds_list) + repeats = [len(conds_list[i]) for i in range(batch_size)] + + if shared.sd_model.model.conditioning_key == "crossattn-adm": + image_uncond = torch.zeros_like(image_cond) + make_condition_dict = lambda c_crossattn, c_adm: {"c_crossattn": [c_crossattn], "c_adm": c_adm} + else: + image_uncond = image_cond + if isinstance(uncond, dict): + make_condition_dict = lambda c_crossattn, c_concat: {**c_crossattn, "c_concat": [c_concat]} + else: + make_condition_dict = lambda c_crossattn, c_concat: {"c_crossattn": [c_crossattn], "c_concat": [c_concat]} + + if not is_edit_model: + x_in = torch.cat([torch.stack([x[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [x]) + sigma_in = torch.cat([torch.stack([sigma[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [sigma]) + image_cond_in = torch.cat([torch.stack([image_cond[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [image_uncond]) + else: + x_in = torch.cat([torch.stack([x[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [x] + [x]) + sigma_in = torch.cat([torch.stack([sigma[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [sigma] + [sigma]) + image_cond_in = torch.cat([torch.stack([image_cond[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [image_uncond] + [torch.zeros_like(self.init_latent)]) + + denoiser_params = CFGDenoiserParams(x_in, image_cond_in, sigma_in, state.sampling_step, state.sampling_steps, tensor, uncond) + cfg_denoiser_callback(denoiser_params) + x_in = denoiser_params.x + image_cond_in = denoiser_params.image_cond + sigma_in = denoiser_params.sigma + tensor = denoiser_params.text_cond + uncond = denoiser_params.text_uncond + skip_uncond = False + + # alternating uncond allows for higher thresholds without the quality loss normally expected from raising it + if self.step % 2 and s_min_uncond > 0 and sigma[0] < s_min_uncond and not is_edit_model: + skip_uncond = True + x_in = x_in[:-batch_size] + sigma_in = sigma_in[:-batch_size] + + self.padded_cond_uncond = False + if shared.opts.pad_cond_uncond and tensor.shape[1] != uncond.shape[1]: + empty = shared.sd_model.cond_stage_model_empty_prompt + num_repeats = (tensor.shape[1] - uncond.shape[1]) // empty.shape[1] + + if num_repeats < 0: + tensor = pad_cond(tensor, -num_repeats, empty) + self.padded_cond_uncond = True + elif num_repeats > 0: + uncond = pad_cond(uncond, num_repeats, empty) + self.padded_cond_uncond = True + + if tensor.shape[1] == uncond.shape[1] or skip_uncond: + if is_edit_model: + cond_in = catenate_conds([tensor, uncond, uncond]) + elif skip_uncond: + cond_in = tensor + else: + cond_in = catenate_conds([tensor, uncond]) + + if shared.batch_cond_uncond: + x_out = self.inner_model(x_in, sigma_in, cond=make_condition_dict(cond_in, image_cond_in)) + else: + x_out = torch.zeros_like(x_in) + for batch_offset in range(0, x_out.shape[0], batch_size): + a = batch_offset + b = a + batch_size + x_out[a:b] = self.inner_model(x_in[a:b], sigma_in[a:b], cond=make_condition_dict(subscript_cond(cond_in, a, b), image_cond_in[a:b])) + else: + x_out = torch.zeros_like(x_in) + batch_size = batch_size*2 if shared.batch_cond_uncond else batch_size + for batch_offset in range(0, tensor.shape[0], batch_size): + a = batch_offset + b = min(a + batch_size, tensor.shape[0]) + + if not is_edit_model: + c_crossattn = subscript_cond(tensor, a, b) + else: + c_crossattn = torch.cat([tensor[a:b]], uncond) + + x_out[a:b] = self.inner_model(x_in[a:b], sigma_in[a:b], cond=make_condition_dict(c_crossattn, image_cond_in[a:b])) + + if not skip_uncond: + x_out[-uncond.shape[0]:] = self.inner_model(x_in[-uncond.shape[0]:], sigma_in[-uncond.shape[0]:], cond=make_condition_dict(uncond, image_cond_in[-uncond.shape[0]:])) + + denoised_image_indexes = [x[0][0] for x in conds_list] + if skip_uncond: + fake_uncond = torch.cat([x_out[i:i+1] for i in denoised_image_indexes]) + x_out = torch.cat([x_out, fake_uncond]) # we skipped uncond denoising, so we put cond-denoised image to where the uncond-denoised image should be + + denoised_params = CFGDenoisedParams(x_out, state.sampling_step, state.sampling_steps, self.inner_model) + cfg_denoised_callback(denoised_params) + + devices.test_for_nans(x_out, "unet") + + if opts.live_preview_content == "Prompt": + sd_samplers_common.store_latent(torch.cat([x_out[i:i+1] for i in denoised_image_indexes])) + elif opts.live_preview_content == "Negative prompt": + sd_samplers_common.store_latent(x_out[-uncond.shape[0]:]) + + if is_edit_model: + denoised = self.combine_denoised_for_edit_model(x_out, cond_scale) + elif skip_uncond: + denoised = self.combine_denoised(x_out, conds_list, uncond, 1.0) + else: + denoised = self.combine_denoised(x_out, conds_list, uncond, cond_scale) + + if self.mask is not None: + denoised = self.init_latent * self.mask + self.nmask * denoised + + after_cfg_callback_params = AfterCFGCallbackParams(denoised, state.sampling_step, state.sampling_steps) + cfg_after_cfg_callback(after_cfg_callback_params) + denoised = after_cfg_callback_params.x + + self.step += 1 + return denoised + + +class TorchHijack: + def __init__(self, sampler_noises): + # Using a deque to efficiently receive the sampler_noises in the same order as the previous index-based + # implementation. + self.sampler_noises = deque(sampler_noises) + + def __getattr__(self, item): + if item == 'randn_like': + return self.randn_like + + if hasattr(torch, item): + return getattr(torch, item) + + raise AttributeError(f"'{type(self).__name__}' object has no attribute '{item}'") + + def randn_like(self, x): + if self.sampler_noises: + noise = self.sampler_noises.popleft() + if noise.shape == x.shape: + return noise + + 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 + -- cgit v1.2.3