from collections import namedtuple, deque import numpy as np from math import floor import torch import tqdm from PIL import Image import inspect import k_diffusion.sampling import torchsde._brownian.brownian_interval import ldm.models.diffusion.ddim import ldm.models.diffusion.plms from modules import prompt_parser, devices, processing, images from modules.shared import opts, cmd_opts, state import modules.shared as shared from modules.script_callbacks import CFGDenoiserParams, cfg_denoiser_callback SamplerData = namedtuple('SamplerData', ['name', 'constructor', 'aliases', 'options']) samplers_k_diffusion = [ ('Euler a', 'sample_euler_ancestral', ['k_euler_a', 'k_euler_ancestral'], {}), ('Euler', 'sample_euler', ['k_euler'], {}), ('LMS', 'sample_lms', ['k_lms'], {}), ('Heun', 'sample_heun', ['k_heun'], {}), ('DPM2', 'sample_dpm_2', ['k_dpm_2'], {'discard_next_to_last_sigma': True}), ('DPM2 a', 'sample_dpm_2_ancestral', ['k_dpm_2_a'], {'discard_next_to_last_sigma': True}), ('DPM++ 2S a', 'sample_dpmpp_2s_ancestral', ['k_dpmpp_2s_a'], {}), ('DPM++ 2M', 'sample_dpmpp_2m', ['k_dpmpp_2m'], {}), ('DPM++ SDE', 'sample_dpmpp_sde', ['k_dpmpp_sde'], {}), ('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', 'discard_next_to_last_sigma': True}), ('DPM2 a Karras', 'sample_dpm_2_ancestral', ['k_dpm_2_a_ka'], {'scheduler': 'karras', 'discard_next_to_last_sigma': True}), ('DPM++ 2S a Karras', 'sample_dpmpp_2s_ancestral', ['k_dpmpp_2s_a_ka'], {'scheduler': 'karras'}), ('DPM++ 2M Karras', 'sample_dpmpp_2m', ['k_dpmpp_2m_ka'], {'scheduler': 'karras'}), ('DPM++ SDE Karras', 'sample_dpmpp_sde', ['k_dpmpp_sde_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), [], {}), ] all_samplers_map = {x.name: x for x in all_samplers} samplers = [] samplers_for_img2img = [] samplers_map = {} def create_sampler(name, model): if name is not None: config = all_samplers_map.get(name, None) else: config = all_samplers[0] assert config is not None, f'bad sampler name: {name}' 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] samplers_map.clear() for sampler in all_samplers: samplers_map[sampler.name.lower()] = sampler.name for alias in sampler.aliases: samplers_map[alias.lower()] = sampler.name 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 single_sample_to_image(sample, approximation=False): if approximation: # https://discuss.huggingface.co/t/decoding-latents-to-rgb-without-upscaling/23204/2 coefs = torch.tensor( [[ 0.298, 0.207, 0.208], [ 0.187, 0.286, 0.173], [-0.158, 0.189, 0.264], [-0.184, -0.271, -0.473]]).to(sample.device) x_sample = torch.einsum("lxy,lr -> rxy", sample, coefs) else: x_sample = processing.decode_first_stage(shared.sd_model, sample.unsqueeze(0))[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 sample_to_image(samples, index=0, approximation=False): return single_sample_to_image(samples[index], approximation) def samples_to_image_grid(samples, approximation=False): return images.image_grid([single_sample_to_image(sample, approximation) for sample in samples]) 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, approximation=opts.show_progress_approximate) class InterruptedException(BaseException): pass 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 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 InterruptedException if self.stop_at is not None and self.step > self.stop_at: raise 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] 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 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 == 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 = 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 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 combine_denoised(self, x_out, conds_list, uncond, cond_scale): 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) return denoised def forward(self, x, sigma, uncond, cond, cond_scale, image_cond): if state.interrupted or state.skipped: raise InterruptedException 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]) image_cond_in = torch.cat([torch.stack([image_cond[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [image_cond]) sigma_in = torch.cat([torch.stack([sigma[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [sigma]) denoiser_params = CFGDenoiserParams(x_in, image_cond_in, sigma_in, state.sampling_step, state.sampling_steps) cfg_denoiser_callback(denoiser_params) x_in = denoiser_params.x image_cond_in = denoiser_params.image_cond sigma_in = denoiser_params.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={"c_crossattn": [cond_in], "c_concat": [image_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={"c_crossattn": [cond_in[a:b]], "c_concat": [image_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={"c_crossattn": [tensor[a:b]], "c_concat": [image_cond_in[a:b]]}) x_out[-uncond.shape[0]:] = self.inner_model(x_in[-uncond.shape[0]:], sigma_in[-uncond.shape[0]:], cond={"c_crossattn": [uncond], "c_concat": [image_cond_in[-uncond.shape[0]:]]}) denoised = self.combine_denoised(x_out, conds_list, uncond, cond_scale) if self.mask is not None: denoised = self.init_latent * self.mask + self.nmask * denoised self.step += 1 return denoised class TorchHijack: def __init__(self, sampler_noises): # Using a deque to efficiently receive the sampler_noises in the same order as the previous index-based # implementation. self.sampler_noises = deque(sampler_noises) def __getattr__(self, item): if item == 'randn_like': return self.randn_like if hasattr(torch, item): return getattr(torch, item) raise AttributeError("'{}' object has no attribute '{}'".format(type(self).__name__, item)) def randn_like(self, x): if self.sampler_noises: noise = self.sampler_noises.popleft() if noise.shape == x.shape: return noise if x.device.type == 'mps': return torch.randn_like(x, device=devices.cpu).to(x.device) else: return torch.randn_like(x) # MPS fix for randn in torchsde def torchsde_randn(size, dtype, device, seed): if device.type == 'mps': generator = torch.Generator(devices.cpu).manual_seed(int(seed)) return torch.randn(size, dtype=dtype, device=devices.cpu, generator=generator).to(device) else: generator = torch.Generator(device).manual_seed(int(seed)) return torch.randn(size, dtype=dtype, device=device, generator=generator) torchsde._brownian.brownian_interval._randn = torchsde_randn class KDiffusionSampler: def __init__(self, funcname, sd_model): denoiser = k_diffusion.external.CompVisVDenoiser if sd_model.parameterization == "v" else k_diffusion.external.CompVisDenoiser self.model_wrap = denoiser(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.stop_at = None self.eta = None self.default_eta = 1.0 self.config = None self.last_latent = None self.conditioning_key = sd_model.model.conditioning_key def callback_state(self, d): step = d['i'] latent = d["denoised"] store_latent(latent) self.last_latent = latent if self.stop_at is not None and step > self.stop_at: raise InterruptedException state.sampling_step = step shared.total_tqdm.update() def launch_sampling(self, steps, func): state.sampling_steps = steps state.sampling_step = 0 try: return func() except InterruptedException: return self.last_latent def number_of_needed_noises(self, p): return p.steps 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.eta = p.eta or opts.eta_ancestral k_diffusion.sampling.torch = TorchHijack(self.sampler_noises if self.sampler_noises is not None else []) 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 get_sigmas(self, 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) if self.config is not None and self.config.options.get('discard_next_to_last_sigma', False): sigmas = torch.cat([sigmas[:-2], sigmas[-1:]]) return sigmas def sample_img2img(self, p, x, noise, conditioning, unconditional_conditioning, steps=None, image_conditioning=None): steps, t_enc = setup_img2img_steps(p, steps) sigmas = self.get_sigmas(p, 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 self.last_latent = x samples = self.launch_sampling(t_enc + 1, lambda: self.func(self.model_wrap_cfg, xi, extra_args={ 'cond': conditioning, 'image_cond': image_conditioning, 'uncond': unconditional_conditioning, 'cond_scale': p.cfg_scale }, disable=False, callback=self.callback_state, **extra_params_kwargs)) return samples def sample(self, p, x, conditioning, unconditional_conditioning, steps=None, image_conditioning = None): steps = steps or p.steps sigmas = self.get_sigmas(p, 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 self.last_latent = x samples = self.launch_sampling(steps, lambda: self.func(self.model_wrap_cfg, x, extra_args={ 'cond': conditioning, 'image_cond': image_conditioning, 'uncond': unconditional_conditioning, 'cond_scale': p.cfg_scale }, disable=False, callback=self.callback_state, **extra_params_kwargs)) return samples