from ldm_patched.k_diffusion import sampling as k_diffusion_sampling from ldm_patched.unipc import uni_pc import torch import enum import collections from ldm_patched.modules import model_management import math from ldm_patched.modules import model_base import ldm_patched.modules.utils import ldm_patched.modules.conds def get_area_and_mult(conds, x_in, timestep_in): area = (x_in.shape[2], x_in.shape[3], 0, 0) strength = 1.0 if 'timestep_start' in conds: timestep_start = conds['timestep_start'] if timestep_in[0] > timestep_start: return None if 'timestep_end' in conds: timestep_end = conds['timestep_end'] if timestep_in[0] < timestep_end: return None if 'area' in conds: area = conds['area'] if 'strength' in conds: strength = conds['strength'] input_x = x_in[:,:,area[2]:area[0] + area[2],area[3]:area[1] + area[3]] if 'mask' in conds: # Scale the mask to the size of the input # The mask should have been resized as we began the sampling process mask_strength = 1.0 if "mask_strength" in conds: mask_strength = conds["mask_strength"] mask = conds['mask'] assert(mask.shape[1] == x_in.shape[2]) assert(mask.shape[2] == x_in.shape[3]) mask = mask[:,area[2]:area[0] + area[2],area[3]:area[1] + area[3]] * mask_strength mask = mask.unsqueeze(1).repeat(input_x.shape[0] // mask.shape[0], input_x.shape[1], 1, 1) else: mask = torch.ones_like(input_x) mult = mask * strength if 'mask' not in conds: rr = 8 if area[2] != 0: for t in range(rr): mult[:,:,t:1+t,:] *= ((1.0/rr) * (t + 1)) if (area[0] + area[2]) < x_in.shape[2]: for t in range(rr): mult[:,:,area[0] - 1 - t:area[0] - t,:] *= ((1.0/rr) * (t + 1)) if area[3] != 0: for t in range(rr): mult[:,:,:,t:1+t] *= ((1.0/rr) * (t + 1)) if (area[1] + area[3]) < x_in.shape[3]: for t in range(rr): mult[:,:,:,area[1] - 1 - t:area[1] - t] *= ((1.0/rr) * (t + 1)) conditioning = {} model_conds = conds["model_conds"] for c in model_conds: conditioning[c] = model_conds[c].process_cond(batch_size=x_in.shape[0], device=x_in.device, area=area) control = conds.get('control', None) patches = None if 'gligen' in conds: gligen = conds['gligen'] patches = {} gligen_type = gligen[0] gligen_model = gligen[1] if gligen_type == "position": gligen_patch = gligen_model.model.set_position(input_x.shape, gligen[2], input_x.device) else: gligen_patch = gligen_model.model.set_empty(input_x.shape, input_x.device) patches['middle_patch'] = [gligen_patch] cond_obj = collections.namedtuple('cond_obj', ['input_x', 'mult', 'conditioning', 'area', 'control', 'patches']) return cond_obj(input_x, mult, conditioning, area, control, patches) def cond_equal_size(c1, c2): if c1 is c2: return True if c1.keys() != c2.keys(): return False for k in c1: if not c1[k].can_concat(c2[k]): return False return True def can_concat_cond(c1, c2): if c1.input_x.shape != c2.input_x.shape: return False def objects_concatable(obj1, obj2): if (obj1 is None) != (obj2 is None): return False if obj1 is not None: if obj1 is not obj2: return False return True if not objects_concatable(c1.control, c2.control): return False if not objects_concatable(c1.patches, c2.patches): return False return cond_equal_size(c1.conditioning, c2.conditioning) def cond_cat(c_list): c_crossattn = [] c_concat = [] c_adm = [] crossattn_max_len = 0 temp = {} for x in c_list: for k in x: cur = temp.get(k, []) cur.append(x[k]) temp[k] = cur out = {} for k in temp: conds = temp[k] out[k] = conds[0].concat(conds[1:]) return out def calc_cond_uncond_batch(model, cond, uncond, x_in, timestep, model_options): out_cond = torch.zeros_like(x_in) out_count = torch.ones_like(x_in) * 1e-37 out_uncond = torch.zeros_like(x_in) out_uncond_count = torch.ones_like(x_in) * 1e-37 COND = 0 UNCOND = 1 to_run = [] for x in cond: p = get_area_and_mult(x, x_in, timestep) if p is None: continue to_run += [(p, COND)] if uncond is not None: for x in uncond: p = get_area_and_mult(x, x_in, timestep) if p is None: continue to_run += [(p, UNCOND)] while len(to_run) > 0: first = to_run[0] first_shape = first[0][0].shape to_batch_temp = [] for x in range(len(to_run)): if can_concat_cond(to_run[x][0], first[0]): to_batch_temp += [x] to_batch_temp.reverse() to_batch = to_batch_temp[:1] free_memory = model_management.get_free_memory(x_in.device) for i in range(1, len(to_batch_temp) + 1): batch_amount = to_batch_temp[:len(to_batch_temp)//i] input_shape = [len(batch_amount) * first_shape[0]] + list(first_shape)[1:] if model.memory_required(input_shape) < free_memory: to_batch = batch_amount break input_x = [] mult = [] c = [] cond_or_uncond = [] area = [] control = None patches = None for x in to_batch: o = to_run.pop(x) p = o[0] input_x.append(p.input_x) mult.append(p.mult) c.append(p.conditioning) area.append(p.area) cond_or_uncond.append(o[1]) control = p.control patches = p.patches batch_chunks = len(cond_or_uncond) input_x = torch.cat(input_x) c = cond_cat(c) timestep_ = torch.cat([timestep] * batch_chunks) if control is not None: c['control'] = control.get_control(input_x, timestep_, c, len(cond_or_uncond)) transformer_options = {} if 'transformer_options' in model_options: transformer_options = model_options['transformer_options'].copy() if patches is not None: if "patches" in transformer_options: cur_patches = transformer_options["patches"].copy() for p in patches: if p in cur_patches: cur_patches[p] = cur_patches[p] + patches[p] else: cur_patches[p] = patches[p] else: transformer_options["patches"] = patches transformer_options["cond_or_uncond"] = cond_or_uncond[:] transformer_options["sigmas"] = timestep c['transformer_options'] = transformer_options if 'model_function_wrapper' in model_options: output = model_options['model_function_wrapper'](model.apply_model, {"input": input_x, "timestep": timestep_, "c": c, "cond_or_uncond": cond_or_uncond}).chunk(batch_chunks) else: output = model.apply_model(input_x, timestep_, **c).chunk(batch_chunks) del input_x for o in range(batch_chunks): if cond_or_uncond[o] == COND: out_cond[:,:,area[o][2]:area[o][0] + area[o][2],area[o][3]:area[o][1] + area[o][3]] += output[o] * mult[o] out_count[:,:,area[o][2]:area[o][0] + area[o][2],area[o][3]:area[o][1] + area[o][3]] += mult[o] else: out_uncond[:,:,area[o][2]:area[o][0] + area[o][2],area[o][3]:area[o][1] + area[o][3]] += output[o] * mult[o] out_uncond_count[:,:,area[o][2]:area[o][0] + area[o][2],area[o][3]:area[o][1] + area[o][3]] += mult[o] del mult out_cond /= out_count del out_count out_uncond /= out_uncond_count del out_uncond_count return out_cond, out_uncond #The main sampling function shared by all the samplers #Returns denoised def sampling_function(model, x, timestep, uncond, cond, cond_scale, model_options={}, seed=None): if math.isclose(cond_scale, 1.0) and model_options.get("disable_cfg1_optimization", False) == False: uncond_ = None else: uncond_ = uncond cond_pred, uncond_pred = calc_cond_uncond_batch(model, cond, uncond_, x, timestep, model_options) if "sampler_cfg_function" in model_options: args = {"cond": x - cond_pred, "uncond": x - uncond_pred, "cond_scale": cond_scale, "timestep": timestep, "input": x, "sigma": timestep, "cond_denoised": cond_pred, "uncond_denoised": uncond_pred, "model": model, "model_options": model_options} cfg_result = x - model_options["sampler_cfg_function"](args) else: cfg_result = uncond_pred + (cond_pred - uncond_pred) * cond_scale for fn in model_options.get("sampler_post_cfg_function", []): args = {"denoised": cfg_result, "cond": cond, "uncond": uncond, "model": model, "uncond_denoised": uncond_pred, "cond_denoised": cond_pred, "sigma": timestep, "model_options": model_options, "input": x} cfg_result = fn(args) return cfg_result class CFGNoisePredictor(torch.nn.Module): def __init__(self, model): super().__init__() self.inner_model = model def apply_model(self, x, timestep, cond, uncond, cond_scale, model_options={}, seed=None): out = sampling_function(self.inner_model, x, timestep, uncond, cond, cond_scale, model_options=model_options, seed=seed) return out def forward(self, *args, **kwargs): return self.apply_model(*args, **kwargs) class KSamplerX0Inpaint(torch.nn.Module): def __init__(self, model): super().__init__() self.inner_model = model def forward(self, x, sigma, uncond, cond, cond_scale, denoise_mask, model_options={}, seed=None): if denoise_mask is not None: latent_mask = 1. - denoise_mask x = x * denoise_mask + (self.latent_image + self.noise * sigma.reshape([sigma.shape[0]] + [1] * (len(self.noise.shape) - 1))) * latent_mask out = self.inner_model(x, sigma, cond=cond, uncond=uncond, cond_scale=cond_scale, model_options=model_options, seed=seed) if denoise_mask is not None: out = out * denoise_mask + self.latent_image * latent_mask return out def simple_scheduler(model, steps): s = model.model_sampling sigs = [] ss = len(s.sigmas) / steps for x in range(steps): sigs += [float(s.sigmas[-(1 + int(x * ss))])] sigs += [0.0] return torch.FloatTensor(sigs) def ddim_scheduler(model, steps): s = model.model_sampling sigs = [] ss = len(s.sigmas) // steps x = 1 while x < len(s.sigmas): sigs += [float(s.sigmas[x])] x += ss sigs = sigs[::-1] sigs += [0.0] return torch.FloatTensor(sigs) def normal_scheduler(model, steps, sgm=False, floor=False): s = model.model_sampling start = s.timestep(s.sigma_max) end = s.timestep(s.sigma_min) if sgm: timesteps = torch.linspace(start, end, steps + 1)[:-1] else: timesteps = torch.linspace(start, end, steps) sigs = [] for x in range(len(timesteps)): ts = timesteps[x] sigs.append(s.sigma(ts)) sigs += [0.0] return torch.FloatTensor(sigs) def get_mask_aabb(masks): if masks.numel() == 0: return torch.zeros((0, 4), device=masks.device, dtype=torch.int) b = masks.shape[0] bounding_boxes = torch.zeros((b, 4), device=masks.device, dtype=torch.int) is_empty = torch.zeros((b), device=masks.device, dtype=torch.bool) for i in range(b): mask = masks[i] if mask.numel() == 0: continue if torch.max(mask != 0) == False: is_empty[i] = True continue y, x = torch.where(mask) bounding_boxes[i, 0] = torch.min(x) bounding_boxes[i, 1] = torch.min(y) bounding_boxes[i, 2] = torch.max(x) bounding_boxes[i, 3] = torch.max(y) return bounding_boxes, is_empty def resolve_areas_and_cond_masks(conditions, h, w, device): # We need to decide on an area outside the sampling loop in order to properly generate opposite areas of equal sizes. # While we're doing this, we can also resolve the mask device and scaling for performance reasons for i in range(len(conditions)): c = conditions[i] if 'area' in c: area = c['area'] if area[0] == "percentage": modified = c.copy() area = (max(1, round(area[1] * h)), max(1, round(area[2] * w)), round(area[3] * h), round(area[4] * w)) modified['area'] = area c = modified conditions[i] = c if 'mask' in c: mask = c['mask'] mask = mask.to(device=device) modified = c.copy() if len(mask.shape) == 2: mask = mask.unsqueeze(0) if mask.shape[1] != h or mask.shape[2] != w: mask = torch.nn.functional.interpolate(mask.unsqueeze(1), size=(h, w), mode='bilinear', align_corners=False).squeeze(1) if modified.get("set_area_to_bounds", False): bounds = torch.max(torch.abs(mask),dim=0).values.unsqueeze(0) boxes, is_empty = get_mask_aabb(bounds) if is_empty[0]: # Use the minimum possible size for efficiency reasons. (Since the mask is all-0, this becomes a noop anyway) modified['area'] = (8, 8, 0, 0) else: box = boxes[0] H, W, Y, X = (box[3] - box[1] + 1, box[2] - box[0] + 1, box[1], box[0]) H = max(8, H) W = max(8, W) area = (int(H), int(W), int(Y), int(X)) modified['area'] = area modified['mask'] = mask conditions[i] = modified def create_cond_with_same_area_if_none(conds, c): if 'area' not in c: return c_area = c['area'] smallest = None for x in conds: if 'area' in x: a = x['area'] if c_area[2] >= a[2] and c_area[3] >= a[3]: if a[0] + a[2] >= c_area[0] + c_area[2]: if a[1] + a[3] >= c_area[1] + c_area[3]: if smallest is None: smallest = x elif 'area' not in smallest: smallest = x else: if smallest['area'][0] * smallest['area'][1] > a[0] * a[1]: smallest = x else: if smallest is None: smallest = x if smallest is None: return if 'area' in smallest: if smallest['area'] == c_area: return out = c.copy() out['model_conds'] = smallest['model_conds'].copy() #TODO: which fields should be copied? conds += [out] def calculate_start_end_timesteps(model, conds): s = model.model_sampling for t in range(len(conds)): x = conds[t] timestep_start = None timestep_end = None if 'start_percent' in x: timestep_start = s.percent_to_sigma(x['start_percent']) if 'end_percent' in x: timestep_end = s.percent_to_sigma(x['end_percent']) if (timestep_start is not None) or (timestep_end is not None): n = x.copy() if (timestep_start is not None): n['timestep_start'] = timestep_start if (timestep_end is not None): n['timestep_end'] = timestep_end conds[t] = n def pre_run_control(model, conds): s = model.model_sampling for t in range(len(conds)): x = conds[t] timestep_start = None timestep_end = None percent_to_timestep_function = lambda a: s.percent_to_sigma(a) if 'control' in x: x['control'].pre_run(model, percent_to_timestep_function) def apply_empty_x_to_equal_area(conds, uncond, name, uncond_fill_func): cond_cnets = [] cond_other = [] uncond_cnets = [] uncond_other = [] for t in range(len(conds)): x = conds[t] if 'area' not in x: if name in x and x[name] is not None: cond_cnets.append(x[name]) else: cond_other.append((x, t)) for t in range(len(uncond)): x = uncond[t] if 'area' not in x: if name in x and x[name] is not None: uncond_cnets.append(x[name]) else: uncond_other.append((x, t)) if len(uncond_cnets) > 0: return for x in range(len(cond_cnets)): temp = uncond_other[x % len(uncond_other)] o = temp[0] if name in o and o[name] is not None: n = o.copy() n[name] = uncond_fill_func(cond_cnets, x) uncond += [n] else: n = o.copy() n[name] = uncond_fill_func(cond_cnets, x) uncond[temp[1]] = n def encode_model_conds(model_function, conds, noise, device, prompt_type, **kwargs): for t in range(len(conds)): x = conds[t] params = x.copy() params["device"] = device params["noise"] = noise params["width"] = params.get("width", noise.shape[3] * 8) params["height"] = params.get("height", noise.shape[2] * 8) params["prompt_type"] = params.get("prompt_type", prompt_type) for k in kwargs: if k not in params: params[k] = kwargs[k] out = model_function(**params) x = x.copy() model_conds = x['model_conds'].copy() for k in out: model_conds[k] = out[k] x['model_conds'] = model_conds conds[t] = x return conds class Sampler: def sample(self): pass def max_denoise(self, model_wrap, sigmas): max_sigma = float(model_wrap.inner_model.model_sampling.sigma_max) sigma = float(sigmas[0]) return math.isclose(max_sigma, sigma, rel_tol=1e-05) or sigma > max_sigma class UNIPC(Sampler): def sample(self, model_wrap, sigmas, extra_args, callback, noise, latent_image=None, denoise_mask=None, disable_pbar=False): return uni_pc.sample_unipc(model_wrap, noise, latent_image, sigmas, max_denoise=self.max_denoise(model_wrap, sigmas), extra_args=extra_args, noise_mask=denoise_mask, callback=callback, disable=disable_pbar) class UNIPCBH2(Sampler): def sample(self, model_wrap, sigmas, extra_args, callback, noise, latent_image=None, denoise_mask=None, disable_pbar=False): return uni_pc.sample_unipc(model_wrap, noise, latent_image, sigmas, max_denoise=self.max_denoise(model_wrap, sigmas), extra_args=extra_args, noise_mask=denoise_mask, callback=callback, variant='bh2', disable=disable_pbar) KSAMPLER_NAMES = ["euler", "euler_ancestral", "heun", "heunpp2","dpm_2", "dpm_2_ancestral", "lms", "dpm_fast", "dpm_adaptive", "dpmpp_2s_ancestral", "dpmpp_sde", "dpmpp_sde_gpu", "dpmpp_2m", "dpmpp_2m_sde", "dpmpp_2m_sde_gpu", "dpmpp_3m_sde", "dpmpp_3m_sde_gpu", "ddpm", "lcm"] class KSAMPLER(Sampler): def __init__(self, sampler_function, extra_options={}, inpaint_options={}): self.sampler_function = sampler_function self.extra_options = extra_options self.inpaint_options = inpaint_options def sample(self, model_wrap, sigmas, extra_args, callback, noise, latent_image=None, denoise_mask=None, disable_pbar=False): extra_args["denoise_mask"] = denoise_mask model_k = KSamplerX0Inpaint(model_wrap) model_k.latent_image = latent_image if self.inpaint_options.get("random", False): #TODO: Should this be the default? generator = torch.manual_seed(extra_args.get("seed", 41) + 1) model_k.noise = torch.randn(noise.shape, generator=generator, device="cpu").to(noise.dtype).to(noise.device) else: model_k.noise = noise if self.max_denoise(model_wrap, sigmas): noise = noise * torch.sqrt(1.0 + sigmas[0] ** 2.0) else: noise = noise * sigmas[0] k_callback = None total_steps = len(sigmas) - 1 if callback is not None: k_callback = lambda x: callback(x["i"], x["denoised"], x["x"], total_steps) if latent_image is not None: noise += latent_image samples = self.sampler_function(model_k, noise, sigmas, extra_args=extra_args, callback=k_callback, disable=disable_pbar, **self.extra_options) return samples def ksampler(sampler_name, extra_options={}, inpaint_options={}): if sampler_name == "dpm_fast": def dpm_fast_function(model, noise, sigmas, extra_args, callback, disable): sigma_min = sigmas[-1] if sigma_min == 0: sigma_min = sigmas[-2] total_steps = len(sigmas) - 1 return k_diffusion_sampling.sample_dpm_fast(model, noise, sigma_min, sigmas[0], total_steps, extra_args=extra_args, callback=callback, disable=disable) sampler_function = dpm_fast_function elif sampler_name == "dpm_adaptive": def dpm_adaptive_function(model, noise, sigmas, extra_args, callback, disable): sigma_min = sigmas[-1] if sigma_min == 0: sigma_min = sigmas[-2] return k_diffusion_sampling.sample_dpm_adaptive(model, noise, sigma_min, sigmas[0], extra_args=extra_args, callback=callback, disable=disable) sampler_function = dpm_adaptive_function else: sampler_function = getattr(k_diffusion_sampling, "sample_{}".format(sampler_name)) return KSAMPLER(sampler_function, extra_options, inpaint_options) def wrap_model(model): model_denoise = CFGNoisePredictor(model) return model_denoise def sample(model, noise, positive, negative, cfg, device, sampler, sigmas, model_options={}, latent_image=None, denoise_mask=None, callback=None, disable_pbar=False, seed=None): positive = positive[:] negative = negative[:] resolve_areas_and_cond_masks(positive, noise.shape[2], noise.shape[3], device) resolve_areas_and_cond_masks(negative, noise.shape[2], noise.shape[3], device) model_wrap = wrap_model(model) calculate_start_end_timesteps(model, negative) calculate_start_end_timesteps(model, positive) if latent_image is not None: latent_image = model.process_latent_in(latent_image) if hasattr(model, 'extra_conds'): positive = encode_model_conds(model.extra_conds, positive, noise, device, "positive", latent_image=latent_image, denoise_mask=denoise_mask) negative = encode_model_conds(model.extra_conds, negative, noise, device, "negative", latent_image=latent_image, denoise_mask=denoise_mask) #make sure each cond area has an opposite one with the same area for c in positive: create_cond_with_same_area_if_none(negative, c) for c in negative: create_cond_with_same_area_if_none(positive, c) pre_run_control(model, negative + positive) apply_empty_x_to_equal_area(list(filter(lambda c: c.get('control_apply_to_uncond', False) == True, positive)), negative, 'control', lambda cond_cnets, x: cond_cnets[x]) apply_empty_x_to_equal_area(positive, negative, 'gligen', lambda cond_cnets, x: cond_cnets[x]) extra_args = {"cond":positive, "uncond":negative, "cond_scale": cfg, "model_options": model_options, "seed":seed} samples = sampler.sample(model_wrap, sigmas, extra_args, callback, noise, latent_image, denoise_mask, disable_pbar) return model.process_latent_out(samples.to(torch.float32)) SCHEDULER_NAMES = ["normal", "karras", "exponential", "sgm_uniform", "simple", "ddim_uniform"] SAMPLER_NAMES = KSAMPLER_NAMES + ["ddim", "uni_pc", "uni_pc_bh2"] def calculate_sigmas_scheduler(model, scheduler_name, steps): if scheduler_name == "karras": sigmas = k_diffusion_sampling.get_sigmas_karras(n=steps, sigma_min=float(model.model_sampling.sigma_min), sigma_max=float(model.model_sampling.sigma_max)) elif scheduler_name == "exponential": sigmas = k_diffusion_sampling.get_sigmas_exponential(n=steps, sigma_min=float(model.model_sampling.sigma_min), sigma_max=float(model.model_sampling.sigma_max)) elif scheduler_name == "normal": sigmas = normal_scheduler(model, steps) elif scheduler_name == "simple": sigmas = simple_scheduler(model, steps) elif scheduler_name == "ddim_uniform": sigmas = ddim_scheduler(model, steps) elif scheduler_name == "sgm_uniform": sigmas = normal_scheduler(model, steps, sgm=True) else: print("error invalid scheduler", self.scheduler) return sigmas def sampler_object(name): if name == "uni_pc": sampler = UNIPC() elif name == "uni_pc_bh2": sampler = UNIPCBH2() elif name == "ddim": sampler = ksampler("euler", inpaint_options={"random": True}) else: sampler = ksampler(name) return sampler class KSampler: SCHEDULERS = SCHEDULER_NAMES SAMPLERS = SAMPLER_NAMES def __init__(self, model, steps, device, sampler=None, scheduler=None, denoise=None, model_options={}): self.model = model self.device = device if scheduler not in self.SCHEDULERS: scheduler = self.SCHEDULERS[0] if sampler not in self.SAMPLERS: sampler = self.SAMPLERS[0] self.scheduler = scheduler self.sampler = sampler self.set_steps(steps, denoise) self.denoise = denoise self.model_options = model_options def calculate_sigmas(self, steps): sigmas = None discard_penultimate_sigma = False if self.sampler in ['dpm_2', 'dpm_2_ancestral', 'uni_pc', 'uni_pc_bh2']: steps += 1 discard_penultimate_sigma = True sigmas = calculate_sigmas_scheduler(self.model, self.scheduler, steps) if discard_penultimate_sigma: sigmas = torch.cat([sigmas[:-2], sigmas[-1:]]) return sigmas def set_steps(self, steps, denoise=None): self.steps = steps if denoise is None or denoise > 0.9999: self.sigmas = self.calculate_sigmas(steps).to(self.device) else: new_steps = int(steps/denoise) sigmas = self.calculate_sigmas(new_steps).to(self.device) self.sigmas = sigmas[-(steps + 1):] def sample(self, noise, positive, negative, cfg, latent_image=None, start_step=None, last_step=None, force_full_denoise=False, denoise_mask=None, sigmas=None, callback=None, disable_pbar=False, seed=None): if sigmas is None: sigmas = self.sigmas if last_step is not None and last_step < (len(sigmas) - 1): sigmas = sigmas[:last_step + 1] if force_full_denoise: sigmas[-1] = 0 if start_step is not None: if start_step < (len(sigmas) - 1): sigmas = sigmas[start_step:] else: if latent_image is not None: return latent_image else: return torch.zeros_like(noise) sampler = sampler_object(self.sampler) return sample(self.model, noise, positive, negative, cfg, self.device, sampler, sigmas, self.model_options, latent_image=latent_image, denoise_mask=denoise_mask, callback=callback, disable_pbar=disable_pbar, seed=seed)