from collections import namedtuple import numpy as np import torch import tqdm from PIL import Image 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']) 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']), ] samplers_data_k_diffusion = [ SamplerData(label, lambda model, funcname=funcname: KDiffusionSampler(funcname, model), aliases) for label, funcname, aliases in samplers_k_diffusion if hasattr(k_diffusion.sampling, funcname) ] 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_for_img2img = [x for x in samplers if x.name != 'PLMS'] sampler_extra_params = { 'sample_euler':['s_churn','s_tmin','s_tmax','s_noise'], 'sample_euler_ancestral':['eta'], 'sample_heun' :['s_churn','s_tmin','s_tmax','s_noise'], 'sample_dpm_2':['s_churn','s_tmin','s_tmax','s_noise'], 'sample_dpm_2_ancestral':['eta'], } 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 for x in tqdm.tqdm(sequence, *args, desc=state.job, file=shared.progress_print_out, **kwargs): if state.interrupted: 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 def number_of_needed_noises(self, p): return 0 def p_sample_ddim_hook(self, x_dec, cond, ts, unconditional_conditioning, *args, **kwargs): cond = prompt_parser.reconstruct_cond_batch(cond, self.step) unconditional_conditioning = prompt_parser.reconstruct_cond_batch(unconditional_conditioning, self.step) 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 sample_img2img(self, p, x, noise, conditioning, unconditional_conditioning, steps=None): steps, t_enc = setup_img2img_steps(p, steps) # existing code fails with cetain step counts, like 9 try: self.sampler.make_schedule(ddim_num_steps=steps, ddim_eta=p.ddim_eta, ddim_discretize=p.ddim_discretize, verbose=False) except Exception: self.sampler.make_schedule(ddim_num_steps=steps+1,ddim_eta=p.ddim_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.sampler.p_sample_ddim = 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 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): 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 = None self.nmask = None 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=p.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=p.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): cond = prompt_parser.reconstruct_cond_batch(cond, self.step) uncond = prompt_parser.reconstruct_cond_batch(uncond, self.step) if shared.batch_cond_uncond: x_in = torch.cat([x] * 2) sigma_in = torch.cat([sigma] * 2) cond_in = torch.cat([uncond, cond]) uncond, cond = self.inner_model(x_in, sigma_in, cond=cond_in).chunk(2) denoised = uncond + (cond - uncond) * cond_scale else: uncond = self.inner_model(x, sigma, cond=uncond) cond = self.inner_model(x, sigma, cond=cond) denoised = uncond + (cond - uncond) * 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 for x in tqdm.trange(count, *args, desc=state.job, file=shared.progress_print_out, **kwargs): if state.interrupted: 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 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 sample_img2img(self, p, x, noise, conditioning, unconditional_conditioning, steps=None): steps, t_enc = setup_img2img_steps(p, steps) sigmas = self.model_wrap.get_sigmas(steps) noise = noise * sigmas[steps - t_enc - 1] xi = x + noise sigma_sched = sigmas[steps - t_enc - 1:] 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.init_latent = x self.model_wrap.step = 0 self.sampler_noise_index = 0 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 val in self.extra_params: if hasattr(p,val): extra_params_kwargs[val] = getattr(p,val) 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 sigmas = self.model_wrap.get_sigmas(steps) x = x * sigmas[0] self.model_wrap_cfg.step = 0 self.sampler_noise_index = 0 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 val in self.extra_params: if hasattr(p,val): extra_params_kwargs[val] = getattr(p,val) samples = self.func(self.model_wrap_cfg, x, sigmas, extra_args={'cond': conditioning, 'uncond': unconditional_conditioning, 'cond_scale': p.cfg_scale}, disable=False, callback=self.callback_state, **extra_params_kwargs) return samples