diff options
Diffstat (limited to 'modules')
| -rw-r--r-- | modules/sd_samplers.py | 3 | ||||
| -rw-r--r-- | modules/sd_samplers_cfg_denoiser.py | 50 | ||||
| -rw-r--r-- | modules/sd_samplers_common.py | 140 | ||||
| -rw-r--r-- | modules/sd_samplers_kdiffusion.py | 152 | ||||
| -rw-r--r-- | modules/sd_samplers_timesteps.py | 147 | ||||
| -rw-r--r-- | modules/sd_samplers_timesteps_impl.py | 135 |
6 files changed, 455 insertions, 172 deletions
diff --git a/modules/sd_samplers.py b/modules/sd_samplers.py index bea2684c..fe206894 100644 --- a/modules/sd_samplers.py +++ b/modules/sd_samplers.py @@ -1,4 +1,4 @@ -from modules import sd_samplers_compvis, sd_samplers_kdiffusion, shared
+from modules import sd_samplers_compvis, sd_samplers_kdiffusion, sd_samplers_timesteps, shared
# imports for functions that previously were here and are used by other modules
from modules.sd_samplers_common import samples_to_image_grid, sample_to_image # noqa: F401
@@ -6,6 +6,7 @@ from modules.sd_samplers_common import samples_to_image_grid, sample_to_image # all_samplers = [
*sd_samplers_kdiffusion.samplers_data_k_diffusion,
*sd_samplers_compvis.samplers_data_compvis,
+ *sd_samplers_timesteps.samplers_data_timesteps,
]
all_samplers_map = {x.name: x for x in all_samplers}
diff --git a/modules/sd_samplers_cfg_denoiser.py b/modules/sd_samplers_cfg_denoiser.py index 33a49783..166a00c7 100644 --- a/modules/sd_samplers_cfg_denoiser.py +++ b/modules/sd_samplers_cfg_denoiser.py @@ -39,7 +39,7 @@ class CFGDenoiser(torch.nn.Module): negative prompt.
"""
- def __init__(self, model):
+ def __init__(self, model, sampler):
super().__init__()
self.inner_model = model
self.mask = None
@@ -48,6 +48,7 @@ class CFGDenoiser(torch.nn.Module): self.step = 0
self.image_cfg_scale = None
self.padded_cond_uncond = False
+ self.sampler = sampler
def combine_denoised(self, x_out, conds_list, uncond, cond_scale):
denoised_uncond = x_out[-uncond.shape[0]:]
@@ -65,6 +66,9 @@ class CFGDenoiser(torch.nn.Module): return denoised
+ def get_pred_x0(self, x_in, x_out, sigma):
+ return x_out
+
def forward(self, x, sigma, uncond, cond, cond_scale, s_min_uncond, image_cond):
if state.interrupted or state.skipped:
raise sd_samplers_common.InterruptedException
@@ -78,6 +82,9 @@ class CFGDenoiser(torch.nn.Module): 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)"
+ if self.mask is not None:
+ x = self.init_latent * self.mask + self.nmask * x
+
batch_size = len(conds_list)
repeats = [len(conds_list[i]) for i in range(batch_size)]
@@ -170,11 +177,6 @@ class CFGDenoiser(torch.nn.Module): devices.test_for_nans(x_out, "unet")
- if opts.live_preview_content == "Prompt":
- sd_samplers_common.store_latent(torch.cat([x_out[i:i+1] for i in denoised_image_indexes]))
- elif opts.live_preview_content == "Negative prompt":
- sd_samplers_common.store_latent(x_out[-uncond.shape[0]:])
-
if is_edit_model:
denoised = self.combine_denoised_for_edit_model(x_out, cond_scale)
elif skip_uncond:
@@ -182,8 +184,16 @@ class CFGDenoiser(torch.nn.Module): else:
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.sampler.last_latent = self.get_pred_x0(torch.cat([x_in[i:i + 1] for i in denoised_image_indexes]), torch.cat([x_out[i:i + 1] for i in denoised_image_indexes]), sigma)
+
+ if opts.live_preview_content == "Prompt":
+ preview = self.sampler.last_latent
+ elif opts.live_preview_content == "Negative prompt":
+ preview = self.get_pred_x0(x_in[-uncond.shape[0]:], x_out[-uncond.shape[0]:], sigma)
+ else:
+ preview = self.get_pred_x0(torch.cat([x_in[i:i+1] for i in denoised_image_indexes]), torch.cat([denoised[i:i+1] for i in denoised_image_indexes]), sigma)
+
+ sd_samplers_common.store_latent(preview)
after_cfg_callback_params = AfterCFGCallbackParams(denoised, state.sampling_step, state.sampling_steps)
cfg_after_cfg_callback(after_cfg_callback_params)
@@ -192,27 +202,3 @@ class CFGDenoiser(torch.nn.Module): 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(f"'{type(self).__name__}' object has no attribute '{item}'")
-
- def randn_like(self, x):
- if self.sampler_noises:
- noise = self.sampler_noises.popleft()
- if noise.shape == x.shape:
- return noise
-
- return devices.randn_like(x)
-
diff --git a/modules/sd_samplers_common.py b/modules/sd_samplers_common.py index 39586b40..adda963b 100644 --- a/modules/sd_samplers_common.py +++ b/modules/sd_samplers_common.py @@ -1,9 +1,11 @@ -from collections import namedtuple
+import inspect
+from collections import namedtuple, deque
import numpy as np
import torch
from PIL import Image
from modules import devices, images, sd_vae_approx, sd_samplers, sd_vae_taesd, shared
from modules.shared import opts, state
+import k_diffusion.sampling
SamplerData = namedtuple('SamplerData', ['name', 'constructor', 'aliases', 'options'])
@@ -127,3 +129,139 @@ def replace_torchsde_browinan(): replace_torchsde_browinan()
+
+
+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(f"'{type(self).__name__}' object has no attribute '{item}'")
+
+ def randn_like(self, x):
+ if self.sampler_noises:
+ noise = self.sampler_noises.popleft()
+ if noise.shape == x.shape:
+ return noise
+
+ return devices.randn_like(x)
+
+
+class Sampler:
+ def __init__(self, funcname):
+ self.funcname = funcname
+ self.func = funcname
+ self.extra_params = []
+ self.sampler_noises = None
+ self.stop_at = None
+ self.eta = None
+ self.config = None # set by the function calling the constructor
+ self.last_latent = None
+ self.s_min_uncond = None
+ self.s_churn = 0.0
+ self.s_tmin = 0.0
+ self.s_tmax = float('inf')
+ self.s_noise = 1.0
+
+ self.eta_option_field = 'eta_ancestral'
+ self.eta_infotext_field = 'Eta'
+
+ self.conditioning_key = shared.sd_model.model.conditioning_key
+
+ self.model_wrap = None
+ self.model_wrap_cfg = None
+
+ def callback_state(self, d):
+ step = d['i']
+
+ 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 RecursionError:
+ print(
+ 'Encountered RecursionError during sampling, returning last latent. '
+ 'rho >5 with a polyexponential scheduler may cause this error. '
+ 'You should try to use a smaller rho value instead.'
+ )
+ return self.last_latent
+ except InterruptedException:
+ return self.last_latent
+
+ def number_of_needed_noises(self, p):
+ return p.steps
+
+ def initialize(self, p) -> dict:
+ 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 getattr(opts, self.eta_option_field, 0.0)
+ self.s_min_uncond = getattr(p, 's_min_uncond', 0.0)
+
+ 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[self.eta_infotext_field] = self.eta
+
+ extra_params_kwargs['eta'] = self.eta
+
+ if len(self.extra_params) > 0:
+ s_churn = getattr(opts, 's_churn', p.s_churn)
+ s_tmin = getattr(opts, 's_tmin', p.s_tmin)
+ s_tmax = getattr(opts, 's_tmax', p.s_tmax) or self.s_tmax # 0 = inf
+ s_noise = getattr(opts, 's_noise', p.s_noise)
+
+ if s_churn != self.s_churn:
+ extra_params_kwargs['s_churn'] = s_churn
+ p.s_churn = s_churn
+ p.extra_generation_params['Sigma churn'] = s_churn
+ if s_tmin != self.s_tmin:
+ extra_params_kwargs['s_tmin'] = s_tmin
+ p.s_tmin = s_tmin
+ p.extra_generation_params['Sigma tmin'] = s_tmin
+ if s_tmax != self.s_tmax:
+ extra_params_kwargs['s_tmax'] = s_tmax
+ p.s_tmax = s_tmax
+ p.extra_generation_params['Sigma tmax'] = s_tmax
+ if s_noise != self.s_noise:
+ extra_params_kwargs['s_noise'] = s_noise
+ p.s_noise = s_noise
+ p.extra_generation_params['Sigma noise'] = s_noise
+
+ return extra_params_kwargs
+
+ 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)
+
+
+
diff --git a/modules/sd_samplers_kdiffusion.py b/modules/sd_samplers_kdiffusion.py index 9c9b46d1..3a2e01b7 100644 --- a/modules/sd_samplers_kdiffusion.py +++ b/modules/sd_samplers_kdiffusion.py @@ -4,8 +4,7 @@ import inspect import k_diffusion.sampling
from modules import devices, sd_samplers_common, sd_samplers_extra, sd_samplers_cfg_denoiser
-from modules.processing import StableDiffusionProcessing
-from modules.shared import opts, state
+from modules.shared import opts
import modules.shared as shared
samplers_k_diffusion = [
@@ -54,133 +53,17 @@ k_diffusion_scheduler = { }
-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(f"'{type(self).__name__}' object has no attribute '{item}'")
-
- def randn_like(self, x):
- if self.sampler_noises:
- noise = self.sampler_noises.popleft()
- if noise.shape == x.shape:
- return noise
+class KDiffusionSampler(sd_samplers_common.Sampler):
+ def __init__(self, funcname, sd_model):
- return devices.randn_like(x)
+ super().__init__(funcname)
+ self.extra_params = sampler_extra_params.get(funcname, [])
+ self.func = funcname if callable(funcname) else getattr(k_diffusion.sampling, self.funcname)
-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 = funcname if callable(funcname) else getattr(k_diffusion.sampling, self.funcname)
- self.extra_params = sampler_extra_params.get(funcname, [])
- self.model_wrap_cfg = sd_samplers_cfg_denoiser.CFGDenoiser(self.model_wrap)
- self.sampler_noises = None
- self.stop_at = None
- self.eta = None
- self.config = None # set by the function calling the constructor
- self.last_latent = None
- self.s_min_uncond = None
-
- # NOTE: These are also defined in the StableDiffusionProcessing class.
- # They should have been here to begin with but we're going to
- # leave that class __init__ signature alone.
- self.s_churn = 0.0
- self.s_tmin = 0.0
- self.s_tmax = float('inf')
- self.s_noise = 1.0
-
- 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 RecursionError:
- print(
- 'Encountered RecursionError during sampling, returning last latent. '
- 'rho >5 with a polyexponential scheduler may cause this error. '
- 'You should try to use a smaller rho value instead.'
- )
- return self.last_latent
- except sd_samplers_common.InterruptedException:
- return self.last_latent
-
- def number_of_needed_noises(self, p):
- return p.steps
-
- def initialize(self, p: StableDiffusionProcessing):
- 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
- self.s_min_uncond = getattr(p, 's_min_uncond', 0.0)
-
- 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
-
- if len(self.extra_params) > 0:
- s_churn = getattr(opts, 's_churn', p.s_churn)
- s_tmin = getattr(opts, 's_tmin', p.s_tmin)
- s_tmax = getattr(opts, 's_tmax', p.s_tmax) or self.s_tmax # 0 = inf
- s_noise = getattr(opts, 's_noise', p.s_noise)
-
- if s_churn != self.s_churn:
- extra_params_kwargs['s_churn'] = s_churn
- p.s_churn = s_churn
- p.extra_generation_params['Sigma churn'] = s_churn
- if s_tmin != self.s_tmin:
- extra_params_kwargs['s_tmin'] = s_tmin
- p.s_tmin = s_tmin
- p.extra_generation_params['Sigma tmin'] = s_tmin
- if s_tmax != self.s_tmax:
- extra_params_kwargs['s_tmax'] = s_tmax
- p.s_tmax = s_tmax
- p.extra_generation_params['Sigma tmax'] = s_tmax
- if s_noise != self.s_noise:
- extra_params_kwargs['s_noise'] = s_noise
- p.s_noise = s_noise
- p.extra_generation_params['Sigma noise'] = s_noise
-
- return extra_params_kwargs
+ self.model_wrap_cfg = sd_samplers_cfg_denoiser.CFGDenoiser(self.model_wrap, self)
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)
@@ -232,22 +115,12 @@ class KDiffusionSampler: 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)
@@ -296,12 +169,14 @@ class KDiffusionSampler: extra_params_kwargs = self.initialize(p)
parameters = inspect.signature(self.func).parameters
+ if 'n' in parameters:
+ extra_params_kwargs['n'] = steps
+
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:
+
+ if 'sigmas' in parameters:
extra_params_kwargs['sigmas'] = sigmas
if self.config.options.get('brownian_noise', False):
@@ -322,3 +197,4 @@ class KDiffusionSampler: return samples
+
diff --git a/modules/sd_samplers_timesteps.py b/modules/sd_samplers_timesteps.py new file mode 100644 index 00000000..8560d009 --- /dev/null +++ b/modules/sd_samplers_timesteps.py @@ -0,0 +1,147 @@ +import torch
+import inspect
+from modules import devices, sd_samplers_common, sd_samplers_timesteps_impl
+from modules.sd_samplers_cfg_denoiser import CFGDenoiser
+
+from modules.shared import opts
+import modules.shared as shared
+
+samplers_timesteps = [
+ ('k_DDIM', sd_samplers_timesteps_impl.ddim, ['k_ddim'], {}),
+ ('k_PLMS', sd_samplers_timesteps_impl.plms, ['k_plms'], {}),
+ ('k_UniPC', sd_samplers_timesteps_impl.unipc, ['k_unipc'], {}),
+]
+
+
+samplers_data_timesteps = [
+ sd_samplers_common.SamplerData(label, lambda model, funcname=funcname: CompVisSampler(funcname, model), aliases, options)
+ for label, funcname, aliases, options in samplers_timesteps
+]
+
+
+class CompVisTimestepsDenoiser(torch.nn.Module):
+ def __init__(self, model, *args, **kwargs):
+ super().__init__(*args, **kwargs)
+ self.inner_model = model
+
+ def forward(self, input, timesteps, **kwargs):
+ return self.inner_model.apply_model(input, timesteps, **kwargs)
+
+
+class CompVisTimestepsVDenoiser(torch.nn.Module):
+ def __init__(self, model, *args, **kwargs):
+ super().__init__(*args, **kwargs)
+ self.inner_model = model
+
+ def predict_eps_from_z_and_v(self, x_t, t, v):
+ return self.inner_model.sqrt_alphas_cumprod[t.to(torch.int), None, None, None] * v + self.inner_model.sqrt_one_minus_alphas_cumprod[t.to(torch.int), None, None, None] * x_t
+
+ def forward(self, input, timesteps, **kwargs):
+ model_output = self.inner_model.apply_model(input, timesteps, **kwargs)
+ e_t = self.predict_eps_from_z_and_v(input, timesteps, model_output)
+ return e_t
+
+
+class CFGDenoiserTimesteps(CFGDenoiser):
+
+ def __init__(self, model, sampler):
+ super().__init__(model, sampler)
+
+ self.alphas = model.inner_model.alphas_cumprod
+
+ def get_pred_x0(self, x_in, x_out, sigma):
+ ts = int(sigma.item())
+
+ s_in = x_in.new_ones([x_in.shape[0]])
+ a_t = self.alphas[ts].item() * s_in
+ sqrt_one_minus_at = (1 - a_t).sqrt()
+
+ pred_x0 = (x_in - sqrt_one_minus_at * x_out) / a_t.sqrt()
+
+ return pred_x0
+
+
+class CompVisSampler(sd_samplers_common.Sampler):
+ def __init__(self, funcname, sd_model):
+ super().__init__(funcname)
+
+ self.eta_option_field = 'eta_ddim'
+ self.eta_infotext_field = 'Eta DDIM'
+
+ denoiser = CompVisTimestepsVDenoiser if sd_model.parameterization == "v" else CompVisTimestepsDenoiser
+ self.model_wrap = denoiser(sd_model)
+ self.model_wrap_cfg = CFGDenoiserTimesteps(self.model_wrap, self)
+
+ def get_timesteps(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
+
+ timesteps = torch.clip(torch.asarray(list(range(0, 1000, 1000 // steps)), device=devices.device) + 1, 0, 999)
+
+ return timesteps
+
+ 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)
+
+ timesteps = self.get_timesteps(p, steps)
+ timesteps_sched = timesteps[:t_enc]
+
+ alphas_cumprod = shared.sd_model.alphas_cumprod
+ sqrt_alpha_cumprod = torch.sqrt(alphas_cumprod[timesteps[t_enc]])
+ sqrt_one_minus_alpha_cumprod = torch.sqrt(1 - alphas_cumprod[timesteps[t_enc]])
+
+ xi = x * sqrt_alpha_cumprod + noise * sqrt_one_minus_alpha_cumprod
+
+ extra_params_kwargs = self.initialize(p)
+ parameters = inspect.signature(self.func).parameters
+
+ if 'timesteps' in parameters:
+ extra_params_kwargs['timesteps'] = timesteps_sched
+ if 'is_img2img' in parameters:
+ extra_params_kwargs['is_img2img'] = True
+
+ 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,
+ 's_min_uncond': self.s_min_uncond
+ }
+
+ 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))
+
+ if self.model_wrap_cfg.padded_cond_uncond:
+ p.extra_generation_params["Pad conds"] = True
+
+ return samples
+
+ def sample(self, p, x, conditioning, unconditional_conditioning, steps=None, image_conditioning=None):
+ steps = steps or p.steps
+ timesteps = self.get_timesteps(p, steps)
+
+ extra_params_kwargs = self.initialize(p)
+ parameters = inspect.signature(self.func).parameters
+
+ if 'timesteps' in parameters:
+ extra_params_kwargs['timesteps'] = timesteps
+
+ 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,
+ 's_min_uncond': self.s_min_uncond
+ }, disable=False, callback=self.callback_state, **extra_params_kwargs))
+
+ if self.model_wrap_cfg.padded_cond_uncond:
+ p.extra_generation_params["Pad conds"] = True
+
+ return samples
+
diff --git a/modules/sd_samplers_timesteps_impl.py b/modules/sd_samplers_timesteps_impl.py new file mode 100644 index 00000000..48d7e649 --- /dev/null +++ b/modules/sd_samplers_timesteps_impl.py @@ -0,0 +1,135 @@ +import torch
+import tqdm
+import k_diffusion.sampling
+import numpy as np
+
+from modules import shared
+from modules.models.diffusion.uni_pc import uni_pc
+
+
+@torch.no_grad()
+def ddim(model, x, timesteps, extra_args=None, callback=None, disable=None, eta=0.0):
+ alphas_cumprod = model.inner_model.inner_model.alphas_cumprod
+ alphas = alphas_cumprod[timesteps]
+ alphas_prev = alphas_cumprod[torch.nn.functional.pad(timesteps[:-1], pad=(1, 0))].to(torch.float64)
+ sqrt_one_minus_alphas = torch.sqrt(1 - alphas)
+ sigmas = eta * np.sqrt((1 - alphas_prev.cpu().numpy()) / (1 - alphas.cpu()) * (1 - alphas.cpu() / alphas_prev.cpu().numpy()))
+
+ extra_args = {} if extra_args is None else extra_args
+ s_in = x.new_ones([x.shape[0]])
+ for i in tqdm.trange(len(timesteps) - 1, disable=disable):
+ index = len(timesteps) - 1 - i
+
+ e_t = model(x, timesteps[index].item() * s_in, **extra_args)
+
+ a_t = alphas[index].item() * s_in
+ a_prev = alphas_prev[index].item() * s_in
+ sigma_t = sigmas[index].item() * s_in
+ sqrt_one_minus_at = sqrt_one_minus_alphas[index].item() * s_in
+
+ pred_x0 = (x - sqrt_one_minus_at * e_t) / a_t.sqrt()
+ dir_xt = (1. - a_prev - sigma_t ** 2).sqrt() * e_t
+ noise = sigma_t * k_diffusion.sampling.torch.randn_like(x)
+ x = a_prev.sqrt() * pred_x0 + dir_xt + noise
+
+ if callback is not None:
+ callback({'x': x, 'i': i, 'sigma': 0, 'sigma_hat': 0, 'denoised': pred_x0})
+
+ return x
+
+
+@torch.no_grad()
+def plms(model, x, timesteps, extra_args=None, callback=None, disable=None):
+ alphas_cumprod = model.inner_model.inner_model.alphas_cumprod
+ alphas = alphas_cumprod[timesteps]
+ alphas_prev = alphas_cumprod[torch.nn.functional.pad(timesteps[:-1], pad=(1, 0))].to(torch.float64)
+ sqrt_one_minus_alphas = torch.sqrt(1 - alphas)
+
+ extra_args = {} if extra_args is None else extra_args
+ s_in = x.new_ones([x.shape[0]])
+ old_eps = []
+
+ def get_x_prev_and_pred_x0(e_t, index):
+ # select parameters corresponding to the currently considered timestep
+ a_t = alphas[index].item() * s_in
+ a_prev = alphas_prev[index].item() * s_in
+ sqrt_one_minus_at = sqrt_one_minus_alphas[index].item() * s_in
+
+ # current prediction for x_0
+ pred_x0 = (x - sqrt_one_minus_at * e_t) / a_t.sqrt()
+
+ # direction pointing to x_t
+ dir_xt = (1. - a_prev).sqrt() * e_t
+ x_prev = a_prev.sqrt() * pred_x0 + dir_xt
+ return x_prev, pred_x0
+
+ for i in tqdm.trange(len(timesteps) - 1, disable=disable):
+ index = len(timesteps) - 1 - i
+ ts = timesteps[index].item() * s_in
+ t_next = timesteps[max(index - 1, 0)].item() * s_in
+
+ e_t = model(x, ts, **extra_args)
+
+ if len(old_eps) == 0:
+ # Pseudo Improved Euler (2nd order)
+ x_prev, pred_x0 = get_x_prev_and_pred_x0(e_t, index)
+ e_t_next = model(x_prev, t_next, **extra_args)
+ e_t_prime = (e_t + e_t_next) / 2
+ elif len(old_eps) == 1:
+ # 2nd order Pseudo Linear Multistep (Adams-Bashforth)
+ e_t_prime = (3 * e_t - old_eps[-1]) / 2
+ elif len(old_eps) == 2:
+ # 3nd order Pseudo Linear Multistep (Adams-Bashforth)
+ e_t_prime = (23 * e_t - 16 * old_eps[-1] + 5 * old_eps[-2]) / 12
+ else:
+ # 4nd order Pseudo Linear Multistep (Adams-Bashforth)
+ e_t_prime = (55 * e_t - 59 * old_eps[-1] + 37 * old_eps[-2] - 9 * old_eps[-3]) / 24
+
+ x_prev, pred_x0 = get_x_prev_and_pred_x0(e_t_prime, index)
+
+ old_eps.append(e_t)
+ if len(old_eps) >= 4:
+ old_eps.pop(0)
+
+ x = x_prev
+
+ if callback is not None:
+ callback({'x': x, 'i': i, 'sigma': 0, 'sigma_hat': 0, 'denoised': pred_x0})
+
+ return x
+
+
+class UniPCCFG(uni_pc.UniPC):
+ def __init__(self, cfg_model, extra_args, callback, *args, **kwargs):
+ super().__init__(None, *args, **kwargs)
+
+ def after_update(x, model_x):
+ callback({'x': x, 'i': self.index, 'sigma': 0, 'sigma_hat': 0, 'denoised': model_x})
+ self.index += 1
+
+ self.cfg_model = cfg_model
+ self.extra_args = extra_args
+ self.callback = callback
+ self.index = 0
+ self.after_update = after_update
+
+ def get_model_input_time(self, t_continuous):
+ return (t_continuous - 1. / self.noise_schedule.total_N) * 1000.
+
+ def model(self, x, t):
+ t_input = self.get_model_input_time(t)
+
+ res = self.cfg_model(x, t_input, **self.extra_args)
+
+ return res
+
+
+def unipc(model, x, timesteps, extra_args=None, callback=None, disable=None, is_img2img=False):
+ alphas_cumprod = model.inner_model.inner_model.alphas_cumprod
+
+ ns = uni_pc.NoiseScheduleVP('discrete', alphas_cumprod=alphas_cumprod)
+ t_start = timesteps[-1] / 1000 + 1 / 1000 if is_img2img else None # this is likely off by a bit - if someone wants to fix it please by all means
+ unipc_sampler = UniPCCFG(model, extra_args, callback, ns, predict_x0=True, thresholding=False, variant=shared.opts.uni_pc_variant)
+ x = unipc_sampler.sample(x, steps=len(timesteps), t_start=t_start, skip_type=shared.opts.uni_pc_skip_type, method="multistep", order=shared.opts.uni_pc_order, lower_order_final=shared.opts.uni_pc_lower_order_final)
+
+ return x
|