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 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', 'DPM fast', 'DPM adaptive']) 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 = shared.sd_model.decode_first_stage(samples[0:1].type(shared.sd_model.dtype))[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 cetain 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 cetin 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) noise = noise * sigmas[steps - t_enc - 1] xi = x + noise extra_params_kwargs = self.initialize(p) sigma_sched = sigmas[steps - t_enc - 1:] self.model_wrap_cfg.init_latent = x return self.func(self.model_wrap_cfg, xi, sigma_sched, 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