301 lines
10 KiB
Python
301 lines
10 KiB
Python
import inspect
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from collections import namedtuple
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import numpy as np
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import torch
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from PIL import Image
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from modules import devices, images, sd_vae_approx, sd_samplers, sd_vae_taesd, shared, sd_models
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from modules.shared import opts, state
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import k_diffusion.sampling
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SamplerData = namedtuple('SamplerData', ['name', 'constructor', 'aliases', 'options'])
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def setup_img2img_steps(p, steps=None):
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if opts.img2img_fix_steps or steps is not None:
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requested_steps = (steps or p.steps)
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steps = int(requested_steps / min(p.denoising_strength, 0.999)) if p.denoising_strength > 0 else 0
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t_enc = requested_steps - 1
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else:
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steps = p.steps
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t_enc = int(min(p.denoising_strength, 0.999) * steps)
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return steps, t_enc
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approximation_indexes = {"Full": 0, "Approx NN": 1, "Approx cheap": 2, "TAESD": 3}
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def samples_to_images_tensor(sample, approximation=None, model=None):
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'''latents -> images [-1, 1]'''
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if approximation is None:
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approximation = approximation_indexes.get(opts.show_progress_type, 0)
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if approximation == 2:
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x_sample = sd_vae_approx.cheap_approximation(sample)
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elif approximation == 1:
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x_sample = sd_vae_approx.model()(sample.to(devices.device, devices.dtype)).detach()
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elif approximation == 3:
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x_sample = sample * 1.5
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x_sample = sd_vae_taesd.decoder_model()(x_sample.to(devices.device, devices.dtype)).detach()
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x_sample = x_sample * 2 - 1
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else:
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if model is None:
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model = shared.sd_model
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x_sample = model.decode_first_stage(sample.to(model.first_stage_model.dtype))
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return x_sample
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def single_sample_to_image(sample, approximation=None):
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x_sample = samples_to_images_tensor(sample.unsqueeze(0), approximation)[0] * 0.5 + 0.5
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x_sample = torch.clamp(x_sample, min=0.0, max=1.0)
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x_sample = 255. * np.moveaxis(x_sample.cpu().numpy(), 0, 2)
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x_sample = x_sample.astype(np.uint8)
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return Image.fromarray(x_sample)
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def decode_first_stage(model, x):
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x = x.to(devices.dtype_vae)
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approx_index = approximation_indexes.get(opts.sd_vae_decode_method, 0)
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return samples_to_images_tensor(x, approx_index, model)
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def sample_to_image(samples, index=0, approximation=None):
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return single_sample_to_image(samples[index], approximation)
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def samples_to_image_grid(samples, approximation=None):
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return images.image_grid([single_sample_to_image(sample, approximation) for sample in samples])
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def images_tensor_to_samples(image, approximation=None, model=None):
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'''image[0, 1] -> latent'''
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if approximation is None:
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approximation = approximation_indexes.get(opts.sd_vae_encode_method, 0)
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if approximation == 3:
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image = image.to(devices.device, devices.dtype)
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x_latent = sd_vae_taesd.encoder_model()(image)
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else:
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if model is None:
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model = shared.sd_model
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image = image.to(shared.device, dtype=devices.dtype_vae)
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image = image * 2 - 1
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x_latent = model.get_first_stage_encoding(model.encode_first_stage(image))
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return x_latent
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def store_latent(decoded):
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state.current_latent = decoded
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if opts.live_previews_enable and opts.show_progress_every_n_steps > 0 and shared.state.sampling_step % opts.show_progress_every_n_steps == 0:
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if not shared.parallel_processing_allowed:
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shared.state.assign_current_image(sample_to_image(decoded))
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def is_sampler_using_eta_noise_seed_delta(p):
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"""returns whether sampler from config will use eta noise seed delta for image creation"""
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sampler_config = sd_samplers.find_sampler_config(p.sampler_name)
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eta = p.eta
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if eta is None and p.sampler is not None:
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eta = p.sampler.eta
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if eta is None and sampler_config is not None:
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eta = 0 if sampler_config.options.get("default_eta_is_0", False) else 1.0
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if eta == 0:
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return False
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return sampler_config.options.get("uses_ensd", False)
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class InterruptedException(BaseException):
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pass
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def replace_torchsde_browinan():
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import torchsde._brownian.brownian_interval
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def torchsde_randn(size, dtype, device, seed):
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return devices.randn_local(seed, size).to(device=device, dtype=dtype)
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torchsde._brownian.brownian_interval._randn = torchsde_randn
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replace_torchsde_browinan()
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def apply_refiner(sampler):
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completed_ratio = sampler.step / sampler.steps
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if completed_ratio <= shared.opts.sd_refiner_switch_at:
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return False
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if shared.opts.sd_refiner_checkpoint == "None":
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return False
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if shared.sd_model.sd_checkpoint_info.title == shared.opts.sd_refiner_checkpoint:
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return False
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refiner_checkpoint_info = sd_models.get_closet_checkpoint_match(shared.opts.sd_refiner_checkpoint)
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if refiner_checkpoint_info is None:
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raise Exception(f'Could not find checkpoint with name {shared.opts.sd_refiner_checkpoint}')
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sampler.p.extra_generation_params['Refiner'] = refiner_checkpoint_info.short_title
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sampler.p.extra_generation_params['Refiner switch at'] = shared.opts.sd_refiner_switch_at
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with sd_models.SkipWritingToConfig():
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sd_models.reload_model_weights(info=refiner_checkpoint_info)
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devices.torch_gc()
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sampler.p.setup_conds()
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sampler.update_inner_model()
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return True
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class TorchHijack:
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"""This is here to replace torch.randn_like of k-diffusion.
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k-diffusion has random_sampler argument for most samplers, but not for all, so
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this is needed to properly replace every use of torch.randn_like.
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We need to replace to make images generated in batches to be same as images generated individually."""
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def __init__(self, p):
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self.rng = p.rng
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def __getattr__(self, item):
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if item == 'randn_like':
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return self.randn_like
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if hasattr(torch, item):
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return getattr(torch, item)
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raise AttributeError(f"'{type(self).__name__}' object has no attribute '{item}'")
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def randn_like(self, x):
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return self.rng.next()
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class Sampler:
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def __init__(self, funcname):
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self.funcname = funcname
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self.func = funcname
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self.extra_params = []
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self.sampler_noises = None
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self.stop_at = None
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self.eta = None
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self.config = None # set by the function calling the constructor
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self.last_latent = None
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self.s_min_uncond = None
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self.s_churn = 0.0
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self.s_tmin = 0.0
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self.s_tmax = float('inf')
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self.s_noise = 1.0
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self.eta_option_field = 'eta_ancestral'
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self.eta_infotext_field = 'Eta'
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self.conditioning_key = shared.sd_model.model.conditioning_key
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self.p = None
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self.model_wrap_cfg = None
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self.sampler_extra_args = None
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def callback_state(self, d):
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step = d['i']
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if self.stop_at is not None and step > self.stop_at:
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raise InterruptedException
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state.sampling_step = step
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shared.total_tqdm.update()
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def launch_sampling(self, steps, func):
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self.model_wrap_cfg.steps = steps
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state.sampling_steps = steps
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state.sampling_step = 0
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try:
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return func()
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except RecursionError:
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print(
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'Encountered RecursionError during sampling, returning last latent. '
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'rho >5 with a polyexponential scheduler may cause this error. '
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'You should try to use a smaller rho value instead.'
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)
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return self.last_latent
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except InterruptedException:
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return self.last_latent
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def number_of_needed_noises(self, p):
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return p.steps
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def initialize(self, p) -> dict:
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self.p = p
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self.model_wrap_cfg.p = p
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self.model_wrap_cfg.mask = p.mask if hasattr(p, 'mask') else None
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self.model_wrap_cfg.nmask = p.nmask if hasattr(p, 'nmask') else None
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self.model_wrap_cfg.step = 0
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self.model_wrap_cfg.image_cfg_scale = getattr(p, 'image_cfg_scale', None)
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self.eta = p.eta if p.eta is not None else getattr(opts, self.eta_option_field, 0.0)
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self.s_min_uncond = getattr(p, 's_min_uncond', 0.0)
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k_diffusion.sampling.torch = TorchHijack(p)
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extra_params_kwargs = {}
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for param_name in self.extra_params:
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if hasattr(p, param_name) and param_name in inspect.signature(self.func).parameters:
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extra_params_kwargs[param_name] = getattr(p, param_name)
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if 'eta' in inspect.signature(self.func).parameters:
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if self.eta != 1.0:
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p.extra_generation_params[self.eta_infotext_field] = self.eta
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extra_params_kwargs['eta'] = self.eta
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if len(self.extra_params) > 0:
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s_churn = getattr(opts, 's_churn', p.s_churn)
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s_tmin = getattr(opts, 's_tmin', p.s_tmin)
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s_tmax = getattr(opts, 's_tmax', p.s_tmax) or self.s_tmax # 0 = inf
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s_noise = getattr(opts, 's_noise', p.s_noise)
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if s_churn != self.s_churn:
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extra_params_kwargs['s_churn'] = s_churn
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p.s_churn = s_churn
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p.extra_generation_params['Sigma churn'] = s_churn
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if s_tmin != self.s_tmin:
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extra_params_kwargs['s_tmin'] = s_tmin
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p.s_tmin = s_tmin
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p.extra_generation_params['Sigma tmin'] = s_tmin
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if s_tmax != self.s_tmax:
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extra_params_kwargs['s_tmax'] = s_tmax
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p.s_tmax = s_tmax
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p.extra_generation_params['Sigma tmax'] = s_tmax
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if s_noise != self.s_noise:
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extra_params_kwargs['s_noise'] = s_noise
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p.s_noise = s_noise
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p.extra_generation_params['Sigma noise'] = s_noise
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return extra_params_kwargs
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def create_noise_sampler(self, x, sigmas, p):
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"""For DPM++ SDE: manually create noise sampler to enable deterministic results across different batch sizes"""
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if shared.opts.no_dpmpp_sde_batch_determinism:
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return None
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from k_diffusion.sampling import BrownianTreeNoiseSampler
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sigma_min, sigma_max = sigmas[sigmas > 0].min(), sigmas.max()
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current_iter_seeds = p.all_seeds[p.iteration * p.batch_size:(p.iteration + 1) * p.batch_size]
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return BrownianTreeNoiseSampler(x, sigma_min, sigma_max, seed=current_iter_seeds)
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