import math import ldm.models.diffusion.ddim import ldm.models.diffusion.plms import numpy as np import torch from modules.shared import state from modules import sd_samplers_common, prompt_parser, shared import modules.models.diffusion.uni_pc samplers_data_compvis = [ sd_samplers_common.SamplerData('DDIM', lambda model: VanillaStableDiffusionSampler(ldm.models.diffusion.ddim.DDIMSampler, model), [], {}), sd_samplers_common.SamplerData('PLMS', lambda model: VanillaStableDiffusionSampler(ldm.models.diffusion.plms.PLMSSampler, model), [], {}), sd_samplers_common.SamplerData('UniPC', lambda model: VanillaStableDiffusionSampler(modules.models.diffusion.uni_pc.UniPCSampler, model), [], {}), ] class VanillaStableDiffusionSampler: def __init__(self, constructor, sd_model): self.sampler = constructor(sd_model) self.is_ddim = hasattr(self.sampler, 'p_sample_ddim') self.is_plms = hasattr(self.sampler, 'p_sample_plms') self.is_unipc = isinstance(self.sampler, modules.models.diffusion.uni_pc.UniPCSampler) self.orig_p_sample_ddim = None if self.is_plms: self.orig_p_sample_ddim = self.sampler.p_sample_plms elif self.is_ddim: self.orig_p_sample_ddim = 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.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): x_dec, ts, cond, unconditional_conditioning = self.before_sample(x_dec, ts, cond, unconditional_conditioning) res = self.orig_p_sample_ddim(x_dec, cond, ts, unconditional_conditioning=unconditional_conditioning, *args, **kwargs) x_dec, ts, cond, unconditional_conditioning, res = self.after_sample(x_dec, ts, cond, unconditional_conditioning, res) return res def before_sample(self, x, ts, cond, unconditional_conditioning): 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 = img_orig * self.mask + self.nmask * x # 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]} return x, ts, cond, unconditional_conditioning def update_step(self, last_latent): if self.mask is not None: self.last_latent = self.init_latent * self.mask + self.nmask * last_latent else: self.last_latent = last_latent sd_samplers_common.store_latent(self.last_latent) self.step += 1 state.sampling_step = self.step shared.total_tqdm.update() def after_sample(self, x, ts, cond, uncond, res): if not self.is_unipc: self.update_step(res[1]) return x, ts, cond, uncond, res def unipc_after_update(self, x, model_x): self.update_step(x) def initialize(self, p): self.eta = p.eta if p.eta is not None else shared.opts.eta_ddim if self.eta != 0.0: p.extra_generation_params["Eta DDIM"] = self.eta if self.is_unipc: keys = [ ('UniPC variant', 'uni_pc_variant'), ('UniPC skip type', 'uni_pc_skip_type'), ('UniPC order', 'uni_pc_order'), ('UniPC lower order final', 'uni_pc_lower_order_final'), ] for name, key in keys: v = getattr(shared.opts, key) if v != shared.opts.get_default(key): p.extra_generation_params[name] = v for fieldname in ['p_sample_ddim', 'p_sample_plms']: if hasattr(self.sampler, fieldname): setattr(self.sampler, fieldname, self.p_sample_ddim_hook) if self.is_unipc: self.sampler.set_hooks(lambda x, t, c, u: self.before_sample(x, t, c, u), lambda x, t, c, u, r: self.after_sample(x, t, c, u, r), lambda x, mx: self.unipc_after_update(x, mx)) 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') or (self.config.name == 'UniPC'): if self.config.name == 'UniPC' and num_steps < shared.opts.uni_pc_order: num_steps = shared.opts.uni_pc_order 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