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| from collections import deque | |
| import torch | |
| import inspect | |
| import einops | |
| import k_diffusion.sampling | |
| from modules import prompt_parser, devices, sd_samplers_common | |
| from modules.shared import opts, state | |
| import modules.shared as shared | |
| from modules.script_callbacks import CFGDenoiserParams, cfg_denoiser_callback | |
| from modules.script_callbacks import CFGDenoisedParams, cfg_denoised_callback | |
| 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 = [ | |
| sd_samplers_common.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) | |
| ] | |
| 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'], | |
| } | |
| class CFGDenoiser(torch.nn.Module): | |
| """ | |
| Classifier free guidance denoiser. A wrapper for stable diffusion model (specifically for unet) | |
| that can take a noisy picture and produce a noise-free picture using two guidances (prompts) | |
| instead of one. Originally, the second prompt is just an empty string, but we use non-empty | |
| negative prompt. | |
| """ | |
| def __init__(self, model): | |
| super().__init__() | |
| self.inner_model = model | |
| self.mask = None | |
| self.nmask = None | |
| self.init_latent = None | |
| self.step = 0 | |
| self.image_cfg_scale = None | |
| 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 combine_denoised_for_edit_model(self, x_out, cond_scale): | |
| out_cond, out_img_cond, out_uncond = x_out.chunk(3) | |
| denoised = out_uncond + cond_scale * (out_cond - out_img_cond) + self.image_cfg_scale * (out_img_cond - out_uncond) | |
| return denoised | |
| def forward(self, x, sigma, uncond, cond, cond_scale, image_cond): | |
| if state.interrupted or state.skipped: | |
| raise sd_samplers_common.InterruptedException | |
| # at self.image_cfg_scale == 1.0 produced results for edit model are the same as with normal sampling, | |
| # so is_edit_model is set to False to support AND composition. | |
| is_edit_model = shared.sd_model.cond_stage_key == "edit" and self.image_cfg_scale is not None and self.image_cfg_scale != 1.0 | |
| conds_list, tensor = prompt_parser.reconstruct_multicond_batch(cond, self.step) | |
| uncond = prompt_parser.reconstruct_cond_batch(uncond, self.step) | |
| assert not is_edit_model or all([len(conds) == 1 for conds in conds_list]), "AND is not supported for InstructPix2Pix checkpoint (unless using Image CFG scale = 1.0)" | |
| batch_size = len(conds_list) | |
| repeats = [len(conds_list[i]) for i in range(batch_size)] | |
| if not is_edit_model: | |
| x_in = torch.cat([torch.stack([x[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [x]) | |
| sigma_in = torch.cat([torch.stack([sigma[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [sigma]) | |
| image_cond_in = torch.cat([torch.stack([image_cond[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [image_cond]) | |
| else: | |
| x_in = torch.cat([torch.stack([x[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [x] + [x]) | |
| sigma_in = torch.cat([torch.stack([sigma[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [sigma] + [sigma]) | |
| image_cond_in = torch.cat([torch.stack([image_cond[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [image_cond] + [torch.zeros_like(self.init_latent)]) | |
| 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]: | |
| if not is_edit_model: | |
| cond_in = torch.cat([tensor, uncond]) | |
| else: | |
| cond_in = torch.cat([tensor, uncond, 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]) | |
| if not is_edit_model: | |
| c_crossattn = [tensor[a:b]] | |
| else: | |
| c_crossattn = torch.cat([tensor[a:b]], uncond) | |
| x_out[a:b] = self.inner_model(x_in[a:b], sigma_in[a:b], cond={"c_crossattn": c_crossattn, "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_params = CFGDenoisedParams(x_out, state.sampling_step, state.sampling_steps) | |
| cfg_denoised_callback(denoised_params) | |
| devices.test_for_nans(x_out, "unet") | |
| if opts.live_preview_content == "Prompt": | |
| sd_samplers_common.store_latent(x_out[0:uncond.shape[0]]) | |
| elif opts.live_preview_content == "Negative prompt": | |
| sd_samplers_common.store_latent(x_out[-uncond.shape[0]:]) | |
| if not is_edit_model: | |
| denoised = self.combine_denoised(x_out, conds_list, uncond, cond_scale) | |
| else: | |
| denoised = self.combine_denoised_for_edit_model(x_out, 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) | |
| 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.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"] | |
| if opts.live_preview_content == "Combined": | |
| sd_samplers_common.store_latent(latent) | |
| self.last_latent = latent | |
| if self.stop_at is not None and step > self.stop_at: | |
| raise sd_samplers_common.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 sd_samplers_common.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_cfg.step = 0 | |
| self.model_wrap_cfg.image_cfg_scale = getattr(p, 'image_cfg_scale', None) | |
| self.eta = p.eta if p.eta is not None else 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: | |
| if self.eta != 1.0: | |
| p.extra_generation_params["Eta"] = self.eta | |
| extra_params_kwargs['eta'] = self.eta | |
| return extra_params_kwargs | |
| def get_sigmas(self, p, steps): | |
| discard_next_to_last_sigma = self.config is not None and self.config.options.get('discard_next_to_last_sigma', False) | |
| if opts.always_discard_next_to_last_sigma and not discard_next_to_last_sigma: | |
| discard_next_to_last_sigma = True | |
| p.extra_generation_params["Discard penultimate sigma"] = True | |
| steps += 1 if discard_next_to_last_sigma else 0 | |
| 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': | |
| sigma_min, sigma_max = (0.1, 10) if opts.use_old_karras_scheduler_sigmas else (self.model_wrap.sigmas[0].item(), self.model_wrap.sigmas[-1].item()) | |
| sigmas = k_diffusion.sampling.get_sigmas_karras(n=steps, sigma_min=sigma_min, sigma_max=sigma_max, device=shared.device) | |
| else: | |
| sigmas = self.model_wrap.get_sigmas(steps) | |
| if discard_next_to_last_sigma: | |
| sigmas = torch.cat([sigmas[:-2], sigmas[-1:]]) | |
| return sigmas | |
| def create_noise_sampler(self, x, sigmas, p): | |
| """For DPM++ SDE: manually create noise sampler to enable deterministic results across different batch sizes""" | |
| if shared.opts.no_dpmpp_sde_batch_determinism: | |
| return None | |
| from k_diffusion.sampling import BrownianTreeNoiseSampler | |
| sigma_min, sigma_max = sigmas[sigmas > 0].min(), sigmas.max() | |
| current_iter_seeds = p.all_seeds[p.iteration * p.batch_size:(p.iteration + 1) * p.batch_size] | |
| return BrownianTreeNoiseSampler(x, sigma_min, sigma_max, seed=current_iter_seeds) | |
| 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) | |
| 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) | |
| parameters = inspect.signature(self.func).parameters | |
| if 'sigma_min' in 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 parameters: | |
| extra_params_kwargs['sigma_max'] = sigma_sched[0] | |
| if 'n' in parameters: | |
| extra_params_kwargs['n'] = len(sigma_sched) - 1 | |
| if 'sigma_sched' in parameters: | |
| extra_params_kwargs['sigma_sched'] = sigma_sched | |
| if 'sigmas' in parameters: | |
| extra_params_kwargs['sigmas'] = sigma_sched | |
| if self.funcname == 'sample_dpmpp_sde': | |
| noise_sampler = self.create_noise_sampler(x, sigmas, p) | |
| extra_params_kwargs['noise_sampler'] = noise_sampler | |
| self.model_wrap_cfg.init_latent = x | |
| self.last_latent = x | |
| extra_args={ | |
| 'cond': conditioning, | |
| 'image_cond': image_conditioning, | |
| 'uncond': unconditional_conditioning, | |
| 'cond_scale': p.cfg_scale, | |
| } | |
| samples = self.launch_sampling(t_enc + 1, lambda: self.func(self.model_wrap_cfg, xi, extra_args=extra_args, 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) | |
| parameters = inspect.signature(self.func).parameters | |
| if 'sigma_min' in 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 parameters: | |
| extra_params_kwargs['n'] = steps | |
| else: | |
| extra_params_kwargs['sigmas'] = sigmas | |
| if self.funcname == 'sample_dpmpp_sde': | |
| noise_sampler = self.create_noise_sampler(x, sigmas, p) | |
| extra_params_kwargs['noise_sampler'] = noise_sampler | |
| 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 | |