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| from collections import namedtuple | |
| import numpy as np | |
| import torch | |
| import tqdm | |
| from PIL import Image | |
| import inspect | |
| import k_diffusion.sampling | |
| import ldm.models.diffusion.ddim | |
| import ldm.models.diffusion.plms | |
| from modules import prompt_parser, devices, processing | |
| from modules.shared import opts, cmd_opts, state | |
| import modules.shared as shared | |
| SamplerData = namedtuple('SamplerData', ['name', 'constructor', 'aliases', 'options']) | |
| samplers_k_diffusion = [ | |
| ('Euler a', 'sample_euler_ancestral', ['k_euler_a'], {}), | |
| ('Euler', 'sample_euler', ['k_euler'], {}), | |
| ('LMS', 'sample_lms', ['k_lms'], {}), | |
| ('Heun', 'sample_heun', ['k_heun'], {}), | |
| ('DPM2', 'sample_dpm_2', ['k_dpm_2'], {}), | |
| ('DPM2 a', 'sample_dpm_2_ancestral', ['k_dpm_2_a'], {}), | |
| ('DPM fast', 'sample_dpm_fast', ['k_dpm_fast'], {}), | |
| ('DPM adaptive', 'sample_dpm_adaptive', ['k_dpm_ad'], {}), | |
| ('LMS Karras', 'sample_lms', ['k_lms_ka'], {'scheduler': 'karras'}), | |
| ('DPM2 Karras', 'sample_dpm_2', ['k_dpm_2_ka'], {'scheduler': 'karras'}), | |
| ('DPM2 a Karras', 'sample_dpm_2_ancestral', ['k_dpm_2_a_ka'], {'scheduler': 'karras'}), | |
| ] | |
| samplers_data_k_diffusion = [ | |
| SamplerData(label, lambda model, funcname=funcname: KDiffusionSampler(funcname, model), aliases, options) | |
| for label, funcname, aliases, options in samplers_k_diffusion | |
| if hasattr(k_diffusion.sampling, funcname) | |
| ] | |
| all_samplers = [ | |
| *samplers_data_k_diffusion, | |
| SamplerData('DDIM', lambda model: VanillaStableDiffusionSampler(ldm.models.diffusion.ddim.DDIMSampler, model), [], {}), | |
| SamplerData('PLMS', lambda model: VanillaStableDiffusionSampler(ldm.models.diffusion.plms.PLMSSampler, model), [], {}), | |
| ] | |
| samplers = [] | |
| samplers_for_img2img = [] | |
| def create_sampler_with_index(list_of_configs, index, model): | |
| config = list_of_configs[index] | |
| sampler = config.constructor(model) | |
| sampler.config = config | |
| return sampler | |
| def set_samplers(): | |
| global samplers, samplers_for_img2img | |
| hidden = set(opts.hide_samplers) | |
| hidden_img2img = set(opts.hide_samplers + ['PLMS']) | |
| samplers = [x for x in all_samplers if x.name not in hidden] | |
| samplers_for_img2img = [x for x in all_samplers if x.name not in hidden_img2img] | |
| set_samplers() | |
| 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'], | |
| } | |
| def setup_img2img_steps(p, steps=None): | |
| if opts.img2img_fix_steps or steps is not None: | |
| steps = int((steps or p.steps) / min(p.denoising_strength, 0.999)) if p.denoising_strength > 0 else 0 | |
| t_enc = p.steps - 1 | |
| else: | |
| steps = p.steps | |
| t_enc = int(min(p.denoising_strength, 0.999) * steps) | |
| return steps, t_enc | |
| def sample_to_image(samples): | |
| x_sample = processing.decode_first_stage(shared.sd_model, samples[0:1])[0] | |
| x_sample = torch.clamp((x_sample + 1.0) / 2.0, min=0.0, max=1.0) | |
| x_sample = 255. * np.moveaxis(x_sample.cpu().numpy(), 0, 2) | |
| x_sample = x_sample.astype(np.uint8) | |
| return Image.fromarray(x_sample) | |
| def store_latent(decoded): | |
| state.current_latent = decoded | |
| if opts.show_progress_every_n_steps > 0 and shared.state.sampling_step % opts.show_progress_every_n_steps == 0: | |
| if not shared.parallel_processing_allowed: | |
| shared.state.current_image = sample_to_image(decoded) | |
| def extended_tdqm(sequence, *args, desc=None, **kwargs): | |
| state.sampling_steps = len(sequence) | |
| state.sampling_step = 0 | |
| seq = sequence if cmd_opts.disable_console_progressbars else tqdm.tqdm(sequence, *args, desc=state.job, file=shared.progress_print_out, **kwargs) | |
| for x in seq: | |
| if state.interrupted or state.skipped: | |
| break | |
| yield x | |
| state.sampling_step += 1 | |
| shared.total_tqdm.update() | |
| ldm.models.diffusion.ddim.tqdm = lambda *args, desc=None, **kwargs: extended_tdqm(*args, desc=desc, **kwargs) | |
| ldm.models.diffusion.plms.tqdm = lambda *args, desc=None, **kwargs: extended_tdqm(*args, desc=desc, **kwargs) | |
| class VanillaStableDiffusionSampler: | |
| def __init__(self, constructor, sd_model): | |
| self.sampler = constructor(sd_model) | |
| self.orig_p_sample_ddim = self.sampler.p_sample_ddim if hasattr(self.sampler, 'p_sample_ddim') else self.sampler.p_sample_plms | |
| self.mask = None | |
| self.nmask = None | |
| self.init_latent = None | |
| self.sampler_noises = None | |
| self.step = 0 | |
| self.eta = None | |
| self.default_eta = 0.0 | |
| self.config = None | |
| def number_of_needed_noises(self, p): | |
| return 0 | |
| def p_sample_ddim_hook(self, x_dec, cond, ts, unconditional_conditioning, *args, **kwargs): | |
| conds_list, tensor = prompt_parser.reconstruct_multicond_batch(cond, self.step) | |
| unconditional_conditioning = prompt_parser.reconstruct_cond_batch(unconditional_conditioning, self.step) | |
| assert all([len(conds) == 1 for conds in conds_list]), 'composition via AND is not supported for DDIM/PLMS samplers' | |
| cond = tensor | |
| # for DDIM, shapes must match, we can't just process cond and uncond independently; | |
| # filling unconditional_conditioning with repeats of the last vector to match length is | |
| # not 100% correct but should work well enough | |
| if unconditional_conditioning.shape[1] < cond.shape[1]: | |
| last_vector = unconditional_conditioning[:, -1:] | |
| last_vector_repeated = last_vector.repeat([1, cond.shape[1] - unconditional_conditioning.shape[1], 1]) | |
| unconditional_conditioning = torch.hstack([unconditional_conditioning, last_vector_repeated]) | |
| elif unconditional_conditioning.shape[1] > cond.shape[1]: | |
| unconditional_conditioning = unconditional_conditioning[:, :cond.shape[1]] | |
| if self.mask is not None: | |
| img_orig = self.sampler.model.q_sample(self.init_latent, ts) | |
| x_dec = img_orig * self.mask + self.nmask * x_dec | |
| res = self.orig_p_sample_ddim(x_dec, cond, ts, unconditional_conditioning=unconditional_conditioning, *args, **kwargs) | |
| if self.mask is not None: | |
| store_latent(self.init_latent * self.mask + self.nmask * res[1]) | |
| else: | |
| store_latent(res[1]) | |
| self.step += 1 | |
| return res | |
| def initialize(self, p): | |
| self.eta = p.eta if p.eta is not None else opts.eta_ddim | |
| for fieldname in ['p_sample_ddim', 'p_sample_plms']: | |
| if hasattr(self.sampler, fieldname): | |
| setattr(self.sampler, fieldname, self.p_sample_ddim_hook) | |
| self.mask = p.mask if hasattr(p, 'mask') else None | |
| self.nmask = p.nmask if hasattr(p, 'nmask') else None | |
| def sample_img2img(self, p, x, noise, conditioning, unconditional_conditioning, steps=None): | |
| steps, t_enc = setup_img2img_steps(p, steps) | |
| self.initialize(p) | |
| # existing code fails with certain step counts, like 9 | |
| try: | |
| self.sampler.make_schedule(ddim_num_steps=steps, ddim_eta=self.eta, ddim_discretize=p.ddim_discretize, verbose=False) | |
| except Exception: | |
| self.sampler.make_schedule(ddim_num_steps=steps+1, ddim_eta=self.eta, ddim_discretize=p.ddim_discretize, verbose=False) | |
| x1 = self.sampler.stochastic_encode(x, torch.tensor([t_enc] * int(x.shape[0])).to(shared.device), noise=noise) | |
| self.init_latent = x | |
| self.step = 0 | |
| samples = self.sampler.decode(x1, conditioning, t_enc, unconditional_guidance_scale=p.cfg_scale, unconditional_conditioning=unconditional_conditioning) | |
| return samples | |
| def sample(self, p, x, conditioning, unconditional_conditioning, steps=None): | |
| self.initialize(p) | |
| self.init_latent = None | |
| self.step = 0 | |
| steps = steps or p.steps | |
| # existing code fails with certain step counts, like 9 | |
| try: | |
| samples_ddim, _ = self.sampler.sample(S=steps, conditioning=conditioning, batch_size=int(x.shape[0]), shape=x[0].shape, verbose=False, unconditional_guidance_scale=p.cfg_scale, unconditional_conditioning=unconditional_conditioning, x_T=x, eta=self.eta) | |
| except Exception: | |
| samples_ddim, _ = self.sampler.sample(S=steps+1, conditioning=conditioning, batch_size=int(x.shape[0]), shape=x[0].shape, verbose=False, unconditional_guidance_scale=p.cfg_scale, unconditional_conditioning=unconditional_conditioning, x_T=x, eta=self.eta) | |
| return samples_ddim | |
| class CFGDenoiser(torch.nn.Module): | |
| def __init__(self, model): | |
| super().__init__() | |
| self.inner_model = model | |
| self.mask = None | |
| self.nmask = None | |
| self.init_latent = None | |
| self.step = 0 | |
| def forward(self, x, sigma, uncond, cond, cond_scale): | |
| conds_list, tensor = prompt_parser.reconstruct_multicond_batch(cond, self.step) | |
| uncond = prompt_parser.reconstruct_cond_batch(uncond, self.step) | |
| batch_size = len(conds_list) | |
| repeats = [len(conds_list[i]) for i in range(batch_size)] | |
| 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]) | |
| if tensor.shape[1] == uncond.shape[1]: | |
| cond_in = torch.cat([tensor, uncond]) | |
| if shared.batch_cond_uncond: | |
| x_out = self.inner_model(x_in, sigma_in, cond=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=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]) | |
| x_out[a:b] = self.inner_model(x_in[a:b], sigma_in[a:b], cond=tensor[a:b]) | |
| x_out[-uncond.shape[0]:] = self.inner_model(x_in[-uncond.shape[0]:], sigma_in[-uncond.shape[0]:], cond=uncond) | |
| 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) | |
| if self.mask is not None: | |
| denoised = self.init_latent * self.mask + self.nmask * denoised | |
| self.step += 1 | |
| return denoised | |
| def extended_trange(sampler, count, *args, **kwargs): | |
| state.sampling_steps = count | |
| state.sampling_step = 0 | |
| seq = range(count) if cmd_opts.disable_console_progressbars else tqdm.trange(count, *args, desc=state.job, file=shared.progress_print_out, **kwargs) | |
| for x in seq: | |
| if state.interrupted or state.skipped: | |
| break | |
| if sampler.stop_at is not None and x > sampler.stop_at: | |
| break | |
| yield x | |
| state.sampling_step += 1 | |
| shared.total_tqdm.update() | |
| class TorchHijack: | |
| def __init__(self, kdiff_sampler): | |
| self.kdiff_sampler = kdiff_sampler | |
| def __getattr__(self, item): | |
| if item == 'randn_like': | |
| return self.kdiff_sampler.randn_like | |
| if hasattr(torch, item): | |
| return getattr(torch, item) | |
| raise AttributeError("'{}' object has no attribute '{}'".format(type(self).__name__, item)) | |
| class KDiffusionSampler: | |
| def __init__(self, funcname, sd_model): | |
| self.model_wrap = k_diffusion.external.CompVisDenoiser(sd_model, quantize=shared.opts.enable_quantization) | |
| self.funcname = funcname | |
| self.func = 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.sampler_noise_index = 0 | |
| self.stop_at = None | |
| self.eta = None | |
| self.default_eta = 1.0 | |
| self.config = None | |
| def callback_state(self, d): | |
| store_latent(d["denoised"]) | |
| def number_of_needed_noises(self, p): | |
| return p.steps | |
| def randn_like(self, x): | |
| noise = self.sampler_noises[self.sampler_noise_index] if self.sampler_noises is not None and self.sampler_noise_index < len(self.sampler_noises) else None | |
| if noise is not None and x.shape == noise.shape: | |
| res = noise | |
| else: | |
| res = torch.randn_like(x) | |
| self.sampler_noise_index += 1 | |
| return res | |
| def initialize(self, p): | |
| 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.step = 0 | |
| self.sampler_noise_index = 0 | |
| self.eta = p.eta or opts.eta_ancestral | |
| if hasattr(k_diffusion.sampling, 'trange'): | |
| k_diffusion.sampling.trange = lambda *args, **kwargs: extended_trange(self, *args, **kwargs) | |
| if self.sampler_noises is not None: | |
| k_diffusion.sampling.torch = TorchHijack(self) | |
| 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: | |
| extra_params_kwargs['eta'] = self.eta | |
| return extra_params_kwargs | |
| def sample_img2img(self, p, x, noise, conditioning, unconditional_conditioning, steps=None): | |
| steps, t_enc = setup_img2img_steps(p, steps) | |
| if p.sampler_noise_scheduler_override: | |
| sigmas = p.sampler_noise_scheduler_override(steps) | |
| elif self.config is not None and self.config.options.get('scheduler', None) == 'karras': | |
| sigmas = k_diffusion.sampling.get_sigmas_karras(n=steps, sigma_min=0.1, sigma_max=10, device=shared.device) | |
| else: | |
| sigmas = self.model_wrap.get_sigmas(steps) | |
| sigma_sched = sigmas[steps - t_enc - 1:] | |
| xi = x + noise * sigma_sched[0] | |
| extra_params_kwargs = self.initialize(p) | |
| if 'sigma_min' in inspect.signature(self.func).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: | |
| extra_params_kwargs['sigma_max'] = sigma_sched[0] | |
| if 'n' in inspect.signature(self.func).parameters: | |
| extra_params_kwargs['n'] = len(sigma_sched) - 1 | |
| if 'sigma_sched' in inspect.signature(self.func).parameters: | |
| extra_params_kwargs['sigma_sched'] = sigma_sched | |
| if 'sigmas' in inspect.signature(self.func).parameters: | |
| extra_params_kwargs['sigmas'] = sigma_sched | |
| self.model_wrap_cfg.init_latent = x | |
| return self.func(self.model_wrap_cfg, xi, extra_args={'cond': conditioning, 'uncond': unconditional_conditioning, 'cond_scale': p.cfg_scale}, disable=False, callback=self.callback_state, **extra_params_kwargs) | |
| def sample(self, p, x, conditioning, unconditional_conditioning, steps=None): | |
| steps = steps or p.steps | |
| if p.sampler_noise_scheduler_override: | |
| sigmas = p.sampler_noise_scheduler_override(steps) | |
| elif self.config is not None and self.config.options.get('scheduler', None) == 'karras': | |
| sigmas = k_diffusion.sampling.get_sigmas_karras(n=steps, sigma_min=0.1, sigma_max=10, device=shared.device) | |
| else: | |
| sigmas = self.model_wrap.get_sigmas(steps) | |
| x = x * sigmas[0] | |
| extra_params_kwargs = self.initialize(p) | |
| if 'sigma_min' in inspect.signature(self.func).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: | |
| extra_params_kwargs['n'] = steps | |
| else: | |
| extra_params_kwargs['sigmas'] = sigmas | |
| samples = self.func(self.model_wrap_cfg, x, extra_args={'cond': conditioning, 'uncond': unconditional_conditioning, 'cond_scale': p.cfg_scale}, disable=False, callback=self.callback_state, **extra_params_kwargs) | |
| return samples | |