177 lines
6.8 KiB
Python
177 lines
6.8 KiB
Python
from collections import namedtuple
|
|
|
|
import ldm.models.diffusion.ddim
|
|
import torch
|
|
import tqdm
|
|
|
|
import k_diffusion.sampling
|
|
import ldm.models.diffusion.ddim
|
|
import ldm.models.diffusion.plms
|
|
|
|
from modules.shared import opts, cmd_opts, state
|
|
import modules.shared as shared
|
|
|
|
|
|
SamplerData = namedtuple('SamplerData', ['name', 'constructor', 'aliases'])
|
|
|
|
samplers_k_diffusion = [
|
|
('Euler a', 'sample_euler_ancestral', ['k_euler_a']),
|
|
('Euler', 'sample_euler', ['k_euler']),
|
|
('LMS', 'sample_lms', ['k_lms']),
|
|
('Heun', 'sample_heun', ['k_heun']),
|
|
('DPM2', 'sample_dpm_2', ['k_dpm_2']),
|
|
('DPM2 a', 'sample_dpm_2_ancestral', ['k_dpm_2_a']),
|
|
]
|
|
|
|
samplers_data_k_diffusion = [
|
|
SamplerData(label, lambda model, funcname=funcname: KDiffusionSampler(funcname, model), aliases)
|
|
for label, funcname, aliases in samplers_k_diffusion
|
|
if hasattr(k_diffusion.sampling, funcname)
|
|
]
|
|
|
|
samplers = [
|
|
*samplers_data_k_diffusion,
|
|
SamplerData('DDIM', lambda model: VanillaStableDiffusionSampler(ldm.models.diffusion.ddim.DDIMSampler, model), []),
|
|
SamplerData('PLMS', lambda model: VanillaStableDiffusionSampler(ldm.models.diffusion.plms.PLMSSampler, model), []),
|
|
]
|
|
samplers_for_img2img = [x for x in samplers if x.name != 'PLMS']
|
|
|
|
|
|
def p_sample_ddim_hook(sampler_wrapper, x_dec, cond, ts, *args, **kwargs):
|
|
if sampler_wrapper.mask is not None:
|
|
img_orig = sampler_wrapper.sampler.model.q_sample(sampler_wrapper.init_latent, ts)
|
|
x_dec = img_orig * sampler_wrapper.mask + sampler_wrapper.nmask * x_dec
|
|
|
|
state.current_latent = x_dec
|
|
|
|
return sampler_wrapper.orig_p_sample_ddim(x_dec, cond, ts, *args, **kwargs)
|
|
|
|
|
|
def extended_tdqm(sequence, *args, desc=None, **kwargs):
|
|
state.sampling_steps = len(sequence)
|
|
state.sampling_step = 0
|
|
|
|
for x in tqdm.tqdm(sequence, *args, desc=state.job, **kwargs):
|
|
if state.interrupted:
|
|
break
|
|
|
|
yield x
|
|
|
|
state.sampling_step += 1
|
|
|
|
|
|
ldm.models.diffusion.ddim.tqdm = lambda *args, desc=None, **kwargs: extended_tdqm(*args, desc=desc, **kwargs)
|
|
ldm.models.diffusion.plms.tqdm = lambda *args, desc=None, **kwargs: extended_tdqm(*args, desc=desc, **kwargs)
|
|
|
|
|
|
class VanillaStableDiffusionSampler:
|
|
def __init__(self, constructor, sd_model):
|
|
self.sampler = constructor(sd_model)
|
|
self.orig_p_sample_ddim = self.sampler.p_sample_ddim if hasattr(self.sampler, 'p_sample_ddim') else None
|
|
self.mask = None
|
|
self.nmask = None
|
|
self.init_latent = None
|
|
|
|
def sample_img2img(self, p, x, noise, conditioning, unconditional_conditioning):
|
|
t_enc = int(min(p.denoising_strength, 0.999) * p.steps)
|
|
|
|
# existing code fails with cetin step counts, like 9
|
|
try:
|
|
self.sampler.make_schedule(ddim_num_steps=p.steps, verbose=False)
|
|
except Exception:
|
|
self.sampler.make_schedule(ddim_num_steps=p.steps+1, verbose=False)
|
|
|
|
x1 = self.sampler.stochastic_encode(x, torch.tensor([t_enc] * int(x.shape[0])).to(shared.device), noise=noise)
|
|
|
|
self.sampler.p_sample_ddim = lambda x_dec, cond, ts, *args, **kwargs: p_sample_ddim_hook(self, x_dec, cond, ts, *args, **kwargs)
|
|
self.mask = p.mask
|
|
self.nmask = p.nmask
|
|
self.init_latent = p.init_latent
|
|
|
|
samples = self.sampler.decode(x1, conditioning, t_enc, unconditional_guidance_scale=p.cfg_scale, unconditional_conditioning=unconditional_conditioning)
|
|
|
|
return samples
|
|
|
|
def sample(self, p, x, conditioning, unconditional_conditioning):
|
|
samples_ddim, _ = self.sampler.sample(S=p.steps, conditioning=conditioning, batch_size=int(x.shape[0]), shape=x[0].shape, verbose=False, unconditional_guidance_scale=p.cfg_scale, unconditional_conditioning=unconditional_conditioning, x_T=x)
|
|
return samples_ddim
|
|
|
|
|
|
class CFGDenoiser(torch.nn.Module):
|
|
def __init__(self, model):
|
|
super().__init__()
|
|
self.inner_model = model
|
|
self.mask = None
|
|
self.nmask = None
|
|
self.init_latent = None
|
|
|
|
def forward(self, x, sigma, uncond, cond, cond_scale):
|
|
if shared.batch_cond_uncond:
|
|
x_in = torch.cat([x] * 2)
|
|
sigma_in = torch.cat([sigma] * 2)
|
|
cond_in = torch.cat([uncond, cond])
|
|
uncond, cond = self.inner_model(x_in, sigma_in, cond=cond_in).chunk(2)
|
|
denoised = uncond + (cond - uncond) * cond_scale
|
|
else:
|
|
uncond = self.inner_model(x, sigma, cond=uncond)
|
|
cond = self.inner_model(x, sigma, cond=cond)
|
|
denoised = uncond + (cond - uncond) * cond_scale
|
|
|
|
if self.mask is not None:
|
|
denoised = self.init_latent * self.mask + self.nmask * denoised
|
|
|
|
return denoised
|
|
|
|
|
|
def extended_trange(count, *args, **kwargs):
|
|
state.sampling_steps = count
|
|
state.sampling_step = 0
|
|
|
|
for x in tqdm.trange(count, *args, desc=state.job, **kwargs):
|
|
if state.interrupted:
|
|
break
|
|
|
|
yield x
|
|
|
|
state.sampling_step += 1
|
|
|
|
|
|
class KDiffusionSampler:
|
|
def __init__(self, funcname, sd_model):
|
|
self.model_wrap = k_diffusion.external.CompVisDenoiser(sd_model)
|
|
self.funcname = funcname
|
|
self.func = getattr(k_diffusion.sampling, self.funcname)
|
|
self.model_wrap_cfg = CFGDenoiser(self.model_wrap)
|
|
|
|
def callback_state(self, d):
|
|
state.current_latent = d["denoised"]
|
|
|
|
def sample_img2img(self, p, x, noise, conditioning, unconditional_conditioning):
|
|
t_enc = int(min(p.denoising_strength, 0.999) * p.steps)
|
|
sigmas = self.model_wrap.get_sigmas(p.steps)
|
|
noise = noise * sigmas[p.steps - t_enc - 1]
|
|
|
|
xi = x + noise
|
|
|
|
sigma_sched = sigmas[p.steps - t_enc - 1:]
|
|
|
|
self.model_wrap_cfg.mask = p.mask
|
|
self.model_wrap_cfg.nmask = p.nmask
|
|
self.model_wrap_cfg.init_latent = p.init_latent
|
|
|
|
if hasattr(k_diffusion.sampling, 'trange'):
|
|
k_diffusion.sampling.trange = lambda *args, **kwargs: extended_trange(*args, **kwargs)
|
|
|
|
return self.func(self.model_wrap_cfg, xi, sigma_sched, extra_args={'cond': conditioning, 'uncond': unconditional_conditioning, 'cond_scale': p.cfg_scale}, disable=False, callback=self.callback_state)
|
|
|
|
def sample(self, p, x, conditioning, unconditional_conditioning):
|
|
sigmas = self.model_wrap.get_sigmas(p.steps)
|
|
x = x * sigmas[0]
|
|
|
|
if hasattr(k_diffusion.sampling, 'trange'):
|
|
k_diffusion.sampling.trange = lambda *args, **kwargs: extended_trange(*args, **kwargs)
|
|
|
|
samples_ddim = self.func(self.model_wrap_cfg, x, sigmas, extra_args={'cond': conditioning, 'uncond': unconditional_conditioning, 'cond_scale': p.cfg_scale}, disable=False, callback=self.callback_state)
|
|
return samples_ddim
|
|
|