270 lines
13 KiB
Python
270 lines
13 KiB
Python
import ast
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import warnings
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import torch
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from tqdm.auto import trange
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from nodes import common_ksampler
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from comfy.k_diffusion import sampling as k_diffusion_sampling
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from comfy.samplers import CompVisVDenoiser
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from comfy.ldm.models.diffusion.ddim import DDIMSampler
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from comfy.utils import ProgressBar
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from .restart_schedulers import SCHEDULER_MAPPING
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def add_restart_segment(restart_segments, n_restart, k, t_min, t_max):
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if restart_segments is None:
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restart_segments = []
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restart_segments.append({'n': n_restart, 'k': k, 't_min': t_min, 't_max': t_max})
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return restart_segments
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def prepare_restart_segments(restart_info):
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try:
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restart_arrays = ast.literal_eval(f"[{restart_info}]")
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except SyntaxError as e:
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print("Ill-formed restart segments")
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raise e
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restart_segments = []
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for arr in restart_arrays:
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if len(arr) != 4:
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raise ValueError("Restart segment must have 4 values")
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n_restart, k, t_min, t_max = arr
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n_restart, k = int(n_restart), int(k)
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restart_segments = add_restart_segment(restart_segments, n_restart, k, t_min, t_max)
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return restart_segments
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def segments_to_timesteps(restart_segments, model):
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timesteps = []
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for segment in restart_segments:
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t_min, t_max = model.sigma_to_t(torch.tensor(
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[segment['t_min'], segment['t_max']], device=model.log_sigmas.device))
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ts_segment = {'n': segment['n'], 'k': segment['k'], 't_min': t_min, 't_max': t_max}
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timesteps.append(ts_segment)
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return timesteps
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def round_restart_segments(ts, restart_segments):
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"""
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Map nearest timestep/sigma min to the nearest timestep/sigma to segments.
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:param ts: Timesteps or sigmas of the original denoising schedule
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:param restart_segments: Restart segments dict of the form {'t_min': t_min, 'n': n, 'k': k, 't_max': t_max}
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:return: dict of the form {nearest_t_min: {'n': n, 'k': k, 't_max': t_max}}
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"""
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t_min_mapping = {}
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for segment in reversed(restart_segments): # Reversed to prioritize segments to the front
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t_min_neighbor = min(ts, key=lambda ts: abs(ts - segment['t_min'])).item()
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if t_min_neighbor == ts[0]:
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warnings.warn(
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f"\n[Restart Sampling] nearest neighbor of segment t_min {segment['t_min']:.4f} is equal to the first t_min in the denoise schedule {ts[0]:.4f}, ignoring segment...", stacklevel=2)
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continue
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if t_min_neighbor > segment['t_max']:
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warnings.warn(
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f"\n[Restart Sampling] t_min neighbor {t_min_neighbor:.4f} is greater than t_max {segment['t_max']:.4f}, ignoring segment...", stacklevel=2)
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continue
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if t_min_neighbor in t_min_mapping:
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warnings.warn(
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f"\n[Restart Sampling] Overwriting segment {t_min_mapping[t_min_neighbor]:.4f}, nearest neighbor of {segment['t_min']:.4f} is {t_min_neighbor}", stacklevel=2)
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t_min_mapping[t_min_neighbor] = {'n': segment['n'], 'k': segment['k'], 't_max': segment['t_max']}
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return t_min_mapping
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def calc_sigmas(scheduler, n, sigma_min, sigma_max, model, device):
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return SCHEDULER_MAPPING[scheduler](model, n, sigma_min, sigma_max, device)
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def calc_restart_steps(restart_segments):
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restart_steps = 0
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for segment in restart_segments.values():
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restart_steps += (segment['n'] - 1) * segment['k']
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return restart_steps
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_total_steps = 0
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_restart_segments = None
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_restart_scheduler = None
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def restart_sampling(model, seed, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, restart_info, restart_scheduler, denoise=1.0, disable_noise=False, start_step=None, last_step=None, force_full_denoise=False):
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global _total_steps, _restart_segments, _restart_scheduler
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_restart_scheduler = restart_scheduler
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_restart_segments = prepare_restart_segments(restart_info)
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if sampler_name == "ddim":
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sampler_wrapper = DDIMWrapper()
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else:
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sampler_wrapper = KSamplerRestartWrapper(sampler_name)
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# Add the additional steps to the progress bar
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pbar_update_absolute = ProgressBar.update_absolute
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def pbar_update_absolute_wrapper(self, value, total=None, preview=None):
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pbar_update_absolute(self, value, _total_steps, preview)
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ProgressBar.update_absolute = pbar_update_absolute_wrapper
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try:
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samples = common_ksampler(model, seed, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, denoise=denoise,
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disable_noise=disable_noise, start_step=start_step, last_step=last_step, force_full_denoise=force_full_denoise)
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finally:
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sampler_wrapper.cleanup()
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ProgressBar.update_absolute = pbar_update_absolute
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return samples
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class OneStepSampler:
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def __init__(self, model, steps, cfg, sampler, scheduler, positive, negative, latent_image, denoise):
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# Keep parameters for sampler
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self.model = model
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self.steps = steps
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self.cfg = cfg
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self.sampler = sampler
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self.scheduler = scheduler
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self.positive = positive
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self.negative = negative
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self.latent_image = latent_image
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self.denoise = denoise
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# Get the sampler function
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if sampler == "ddim":
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sampler = DDIMSampler(self.model, device=self.device)
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sampler.make_schedule_timesteps(ddim_timesteps=timesteps, verbose=False)
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z_enc = sampler.stochastic_encode(latent_image, torch.tensor(
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[len(timesteps) - 1] * noise.shape[0]).to(self.device), noise=noise, max_denoise=max_denoise)
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samples, _ = sampler.sample_custom(ddim_timesteps=timesteps,
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conditioning=positive,
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batch_size=noise.shape[0],
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shape=noise.shape[1:],
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verbose=False,
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unconditional_guidance_scale=cfg,
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unconditional_conditioning=negative,
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eta=0.0,
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x_T=z_enc,
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x0=latent_image,
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img_callback=ddim_callback,
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denoise_function=sampling_function,
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extra_args=extra_args,
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mask=noise_mask,
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to_zero=sigmas[-1] == 0,
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end_step=sigmas.shape[0] - 1,
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disable_pbar=disable_pbar)
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else:
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sample = getattr(k_diffusion_sampling, f"sample_{sampler}")
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class RestartWrapper:
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def cleanup(self):
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pass
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class KSamplerRestartWrapper(RestartWrapper):
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ksampler = None
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def __init__(self, sampler_name):
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self.sample_func_name = "sample_{}".format(sampler_name)
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KSamplerRestartWrapper.ksampler = getattr(k_diffusion_sampling, self.sample_func_name)
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setattr(k_diffusion_sampling, self.sample_func_name, self.ksampler_restart_wrapper)
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def cleanup(self):
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setattr(k_diffusion_sampling, self.sample_func_name, KSamplerRestartWrapper.ksampler)
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@staticmethod
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@torch.no_grad()
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def ksampler_restart_wrapper(model, x, sigmas, extra_args=None, callback=None, disable=None):
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global _total_steps, _restart_segments, _restart_scheduler
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ksampler = KSamplerRestartWrapper.ksampler
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segments = round_restart_segments(sigmas, _restart_segments)
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_total_steps = len(sigmas) - 1 + calc_restart_steps(segments)
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step = 0
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def callback_wrapper(x):
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x["i"] = step
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if callback is not None:
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callback(x)
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with trange(_total_steps, disable=disable) as pbar:
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for i in range(len(sigmas) - 1):
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x = ksampler(model, x, torch.tensor([sigmas[i], sigmas[i + 1]],
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device=x.device), extra_args, callback_wrapper, True)
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pbar.update(1)
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step += 1
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if sigmas[i + 1].item() in segments:
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seg = segments[sigmas[i + 1].item()]
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s_min, s_max, k, n_restart = sigmas[i + 1], seg['t_max'], seg['k'], seg['n']
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seg_sigmas = calc_sigmas(_restart_scheduler, n_restart, s_min,
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s_max, model.inner_model, device=x.device)
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for _ in range(k):
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x += torch.randn_like(x) * (s_max ** 2 - s_min ** 2) ** 0.5
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for j in range(n_restart - 1):
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x = ksampler(model, x, torch.tensor(
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[seg_sigmas[j], seg_sigmas[j + 1]], device=x.device), extra_args, callback_wrapper, True)
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pbar.update(1)
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step += 1
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return x
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class DDIMWrapper(RestartWrapper):
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def __init__(self):
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self.__class__.sample_custom = DDIMSampler.sample_custom
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DDIMSampler.sample_custom = self.ddim_wrapper
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def cleanup(self):
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DDIMSampler.sample_custom = self.__class__.sample_custom
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@staticmethod
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@torch.no_grad()
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def ddim_wrapper(self, ddim_timesteps, conditioning=None, callback=None, img_callback=None, quantize_x0=False,
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eta=0., mask=None, x0=None, temperature=1., noise_dropout=0., score_corrector=None,
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corrector_kwargs=None, verbose=True, x_T=None, log_every_t=100, unconditional_guidance_scale=1.,
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unconditional_conditioning=None, dynamic_threshold=None, ucg_schedule=None, denoise_function=None,
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extra_args=None, to_zero=True, end_step=None, disable_pbar=False, **kwargs):
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global _total_steps, _restart_segments, _restart_scheduler
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ddim_sampler = __class__.sample_custom
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model_denoise = CompVisVDenoiser(self.model)
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segments = segments_to_timesteps(_restart_segments, model_denoise)
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segments = round_restart_segments(ddim_timesteps, segments)
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_total_steps = len(ddim_timesteps) - 1 + calc_restart_steps(segments)
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step = 0
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def callback_wrapper(pred_x0, i):
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img_callback(pred_x0, step)
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def ddim_simplified(x, timesteps, x_T=None, disable_pbar=False):
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if x_T is None:
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self.make_schedule_timesteps(ddim_timesteps=timesteps, verbose=False)
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x_T = self.stochastic_encode(x, torch.tensor(
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[len(timesteps) - 1] * x.shape[0]).to(self.device), noise=torch.zeros_like(x), max_denoise=False)
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x, intermediates = ddim_sampler(
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self, timesteps, conditioning, callback=callback, img_callback=callback_wrapper, quantize_x0=quantize_x0,
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eta=eta, mask=mask, x0=x, temperature=temperature, noise_dropout=noise_dropout, score_corrector=score_corrector,
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corrector_kwargs=corrector_kwargs, verbose=verbose, x_T=x_T, log_every_t=log_every_t,
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unconditional_guidance_scale=unconditional_guidance_scale, unconditional_conditioning=unconditional_conditioning,
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dynamic_threshold=dynamic_threshold, ucg_schedule=ucg_schedule, denoise_function=denoise_function, extra_args=extra_args,
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to_zero=timesteps[0].item() == 0, end_step=len(timesteps) - 1, disable_pbar=disable_pbar
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)
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return x, intermediates
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with trange(_total_steps, disable=disable_pbar) as pbar:
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for i in reversed(range(len(ddim_timesteps) - 1)):
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x0, intermediates = ddim_simplified(x0, ddim_timesteps[i:i + 2], x_T=x_T, disable_pbar=True)
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x_T = None
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pbar.update(1)
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step += 1
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if ddim_timesteps[i].item() in segments:
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seg = segments[ddim_timesteps[i].item()]
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t_min, t_max, k, n_restart = ddim_timesteps[i], seg['t_max'], seg['k'], seg['n']
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s_min, s_max = model_denoise.t_to_sigma(t_min), model_denoise.t_to_sigma(t_max)
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seg_sigmas = calc_sigmas(_restart_scheduler, n_restart, s_min,
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s_max, model_denoise, device=x0.device)
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for _ in range(k):
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x0 += torch.randn_like(x0) * (s_max ** 2 - s_min ** 2) ** 0.5
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for j in range(n_restart - 1):
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seg_ts = model_denoise.sigma_to_t(seg_sigmas[j]).to(torch.int32)
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seg_ts_next = model_denoise.sigma_to_t(seg_sigmas[j + 1]).to(torch.int32)
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x0, intermediates = ddim_simplified(x0, [seg_ts_next, seg_ts], disable_pbar=True)
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pbar.update(1)
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step += 1
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return x0, intermediates
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