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from collections import namedtuple
import numpy as np
import torch
import tqdm
from PIL import Image
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 sample_to_image(samples):
x_sample = shared.sd_model.decode_first_stage(samples[0:1].type(shared.sd_model.dtype))[0]
x_sample = torch.clamp((x_sample + 1.0) / 2.0, min=0.0, max=1.0)
x_sample = 255. * np.moveaxis(x_sample.cpu().numpy(), 0, 2)
x_sample = x_sample.astype(np.uint8)
return Image.fromarray(x_sample)
def store_latent(decoded):
state.current_latent = decoded
if opts.show_progress_every_n_steps > 0 and shared.state.sampling_step % opts.show_progress_every_n_steps == 0:
if not shared.parallel_processing_allowed:
shared.state.current_image = sample_to_image(decoded)
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
res = sampler_wrapper.orig_p_sample_ddim(x_dec, cond, ts, *args, **kwargs)
if sampler_wrapper.mask is not None:
store_latent(sampler_wrapper.init_latent * sampler_wrapper.mask + sampler_wrapper.nmask * res[1])
else:
store_latent(res[1])
return res
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, file=shared.progress_print_out, **kwargs):
if state.interrupted:
break
yield x
state.sampling_step += 1
shared.total_tqdm.update()
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 self.sampler.p_sample_plms
self.mask = None
self.nmask = None
self.init_latent = None
self.sampler_noises = None
def number_of_needed_noises(self, p):
return 0
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 cetain 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):
for fieldname in ['p_sample_ddim', 'p_sample_plms']:
if hasattr(self.sampler, fieldname):
setattr(self.sampler, fieldname, lambda x_dec, cond, ts, *args, **kwargs: p_sample_ddim_hook(self, x_dec, cond, ts, *args, **kwargs))
self.mask = None
self.nmask = None
self.init_latent = None
# existing code fails with cetin step counts, like 9
try:
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)
except Exception:
samples_ddim, _ = self.sampler.sample(S=p.steps+1, 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, file=shared.progress_print_out, **kwargs):
if state.interrupted:
break
yield x
state.sampling_step += 1
shared.total_tqdm.update()
class TorchHijack:
def __init__(self, kdiff_sampler):
self.kdiff_sampler = kdiff_sampler
def __getattr__(self, item):
if item == 'randn_like':
return self.kdiff_sampler.randn_like
if hasattr(torch, item):
return getattr(torch, item)
raise AttributeError("'{}' object has no attribute '{}'".format(type(self).__name__, item))
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)
self.sampler_noises = None
self.sampler_noise_index = 0
def callback_state(self, d):
store_latent(d["denoised"])
def number_of_needed_noises(self, p):
return p.steps
def randn_like(self, x):
noise = self.sampler_noises[self.sampler_noise_index] if self.sampler_noises is not None and self.sampler_noise_index < len(self.sampler_noises) else None
if noise is not None and x.shape == noise.shape:
res = noise
else:
res = torch.randn_like(x)
self.sampler_noise_index += 1
return res
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)
if self.sampler_noises is not None:
k_diffusion.sampling.torch = TorchHijack(self)
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)
if self.sampler_noises is not None:
k_diffusion.sampling.torch = TorchHijack(self)
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
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