167 lines
6.3 KiB
Python
167 lines
6.3 KiB
Python
import torch
|
|
import inspect
|
|
import sys
|
|
from modules import devices, sd_samplers_common, sd_samplers_timesteps_impl
|
|
from modules.sd_samplers_cfg_denoiser import CFGDenoiser
|
|
from modules.script_callbacks import ExtraNoiseParams, extra_noise_callback
|
|
|
|
from modules.shared import opts
|
|
import modules.shared as shared
|
|
|
|
samplers_timesteps = [
|
|
('DDIM', sd_samplers_timesteps_impl.ddim, ['ddim'], {}),
|
|
('PLMS', sd_samplers_timesteps_impl.plms, ['plms'], {}),
|
|
('UniPC', sd_samplers_timesteps_impl.unipc, ['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 torch.sqrt(self.inner_model.alphas_cumprod)[t.to(torch.int), None, None, None] * v + torch.sqrt(1 - self.inner_model.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, sampler):
|
|
super().__init__(sampler)
|
|
|
|
self.alphas = shared.sd_model.alphas_cumprod
|
|
self.mask_before_denoising = True
|
|
|
|
def get_pred_x0(self, x_in, x_out, sigma):
|
|
ts = sigma.to(dtype=int)
|
|
|
|
a_t = self.alphas[ts][:, None, None, None]
|
|
sqrt_one_minus_at = (1 - a_t).sqrt()
|
|
|
|
pred_x0 = (x_in - sqrt_one_minus_at * x_out) / a_t.sqrt()
|
|
|
|
return pred_x0
|
|
|
|
@property
|
|
def inner_model(self):
|
|
if self.model_wrap is None:
|
|
denoiser = CompVisTimestepsVDenoiser if shared.sd_model.parameterization == "v" else CompVisTimestepsDenoiser
|
|
self.model_wrap = denoiser(shared.sd_model)
|
|
|
|
return self.model_wrap
|
|
|
|
|
|
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'
|
|
self.eta_default = 0.0
|
|
|
|
self.model_wrap_cfg = CFGDenoiserTimesteps(self)
|
|
self.model_wrap = self.model_wrap_cfg.inner_model
|
|
|
|
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
|
|
|
|
if opts.img2img_extra_noise > 0:
|
|
p.extra_generation_params["Extra noise"] = opts.img2img_extra_noise
|
|
extra_noise_params = ExtraNoiseParams(noise, x, xi)
|
|
extra_noise_callback(extra_noise_params)
|
|
noise = extra_noise_params.noise
|
|
xi += noise * opts.img2img_extra_noise * sqrt_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
|
|
self.sampler_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=self.sampler_extra_args, disable=False, callback=self.callback_state, **extra_params_kwargs))
|
|
|
|
self.add_infotext(p)
|
|
|
|
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
|
|
self.sampler_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(steps, lambda: self.func(self.model_wrap_cfg, x, extra_args=self.sampler_extra_args, disable=False, callback=self.callback_state, **extra_params_kwargs))
|
|
|
|
self.add_infotext(p)
|
|
|
|
return samples
|
|
|
|
|
|
sys.modules['modules.sd_samplers_compvis'] = sys.modules[__name__]
|
|
VanillaStableDiffusionSampler = CompVisSampler # temp. compatibility with older extensions
|