import math import numpy as np import torch from modules.shared import state from modules import sd_samplers_common, prompt_parser, shared class VanillaStableDiffusionSampler: def __init__(self, constructor, sd_model): self.sampler = constructor(sd_model) self.is_plms = hasattr(self.sampler, 'p_sample_plms') self.orig_p_sample_ddim = self.sampler.p_sample_plms if self.is_plms else self.sampler.p_sample_ddim self.mask = None self.nmask = None self.init_latent = None self.sampler_noises = None self.step = 0 self.stop_at = None self.eta = None self.default_eta = 0.0 self.config = None self.last_latent = None self.conditioning_key = sd_model.model.conditioning_key def number_of_needed_noises(self, p): return 0 def launch_sampling(self, steps, func): state.sampling_steps = steps state.sampling_step = 0 try: return func() except sd_samplers_common.InterruptedException: return self.last_latent def p_sample_ddim_hook(self, x_dec, cond, ts, unconditional_conditioning, *args, **kwargs): if state.interrupted or state.skipped: raise sd_samplers_common.InterruptedException if self.stop_at is not None and self.step > self.stop_at: raise sd_samplers_common.InterruptedException # Have to unwrap the inpainting conditioning here to perform pre-processing image_conditioning = None if isinstance(cond, dict): image_conditioning = cond["c_concat"][0] cond = cond["c_crossattn"][0] unconditional_conditioning = unconditional_conditioning["c_crossattn"][0] 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 # Wrap the image conditioning back up since the DDIM code can accept the dict directly. # Note that they need to be lists because it just concatenates them later. if image_conditioning is not None: cond = {"c_concat": [image_conditioning], "c_crossattn": [cond]} unconditional_conditioning = {"c_concat": [image_conditioning], "c_crossattn": [unconditional_conditioning]} res = self.orig_p_sample_ddim(x_dec, cond, ts, unconditional_conditioning=unconditional_conditioning, *args, **kwargs) if self.mask is not None: self.last_latent = self.init_latent * self.mask + self.nmask * res[1] else: self.last_latent = res[1] sd_samplers_common.store_latent(self.last_latent) self.step += 1 state.sampling_step = self.step shared.total_tqdm.update() return res def initialize(self, p): self.eta = p.eta if p.eta is not None else shared.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 adjust_steps_if_invalid(self, p, num_steps): if (self.config.name == 'DDIM' and p.ddim_discretize == 'uniform') or (self.config.name == 'PLMS'): valid_step = 999 / (1000 // num_steps) if valid_step == math.floor(valid_step): return int(valid_step) + 1 return num_steps 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) steps = self.adjust_steps_if_invalid(p, steps) self.initialize(p) self.sampler.make_schedule(ddim_num_steps=steps, 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.last_latent = x self.step = 0 # Wrap the conditioning models with additional image conditioning for inpainting model if image_conditioning is not None: conditioning = {"c_concat": [image_conditioning], "c_crossattn": [conditioning]} unconditional_conditioning = {"c_concat": [image_conditioning], "c_crossattn": [unconditional_conditioning]} samples = self.launch_sampling(t_enc + 1, lambda: 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, image_conditioning=None): self.initialize(p) self.init_latent = None self.last_latent = x self.step = 0 steps = self.adjust_steps_if_invalid(p, steps or p.steps) # Wrap the conditioning models with additional image conditioning for inpainting model # dummy_for_plms is needed because PLMS code checks the first item in the dict to have the right shape if image_conditioning is not None: conditioning = {"dummy_for_plms": np.zeros((conditioning.shape[0],)), "c_crossattn": [conditioning], "c_concat": [image_conditioning]} unconditional_conditioning = {"c_crossattn": [unconditional_conditioning], "c_concat": [image_conditioning]} samples_ddim = self.launch_sampling(steps, lambda: 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)[0]) return samples_ddim