mirror of
https://github.com/openvinotoolkit/stable-diffusion-webui.git
synced 2024-12-15 15:13:45 +03:00
511 lines
24 KiB
Python
511 lines
24 KiB
Python
from collections import deque
|
|
import torch
|
|
import inspect
|
|
import k_diffusion.sampling
|
|
from modules import prompt_parser, devices, sd_samplers_common
|
|
|
|
from modules.shared import opts, state
|
|
import modules.shared as shared
|
|
from modules.script_callbacks import CFGDenoiserParams, cfg_denoiser_callback
|
|
from modules.script_callbacks import CFGDenoisedParams, cfg_denoised_callback
|
|
from modules.script_callbacks import AfterCFGCallbackParams, cfg_after_cfg_callback
|
|
|
|
samplers_k_diffusion = [
|
|
('Euler a', 'sample_euler_ancestral', ['k_euler_a', 'k_euler_ancestral'], {"uses_ensd": True}),
|
|
('Euler', 'sample_euler', ['k_euler'], {}),
|
|
('LMS', 'sample_lms', ['k_lms'], {}),
|
|
('Heun', 'sample_heun', ['k_heun'], {"second_order": True}),
|
|
('DPM2', 'sample_dpm_2', ['k_dpm_2'], {'discard_next_to_last_sigma': True}),
|
|
('DPM2 a', 'sample_dpm_2_ancestral', ['k_dpm_2_a'], {'discard_next_to_last_sigma': True, "uses_ensd": True}),
|
|
('DPM++ 2S a', 'sample_dpmpp_2s_ancestral', ['k_dpmpp_2s_a'], {"uses_ensd": True, "second_order": True}),
|
|
('DPM++ 2M', 'sample_dpmpp_2m', ['k_dpmpp_2m'], {}),
|
|
('DPM++ SDE', 'sample_dpmpp_sde', ['k_dpmpp_sde'], {"second_order": True, "brownian_noise": True}),
|
|
('DPM++ 2M SDE', 'sample_dpmpp_2m_sde', ['k_dpmpp_2m_sde_ka'], {"brownian_noise": True}),
|
|
('DPM fast', 'sample_dpm_fast', ['k_dpm_fast'], {"uses_ensd": True}),
|
|
('DPM adaptive', 'sample_dpm_adaptive', ['k_dpm_ad'], {"uses_ensd": True}),
|
|
('LMS Karras', 'sample_lms', ['k_lms_ka'], {'scheduler': 'karras'}),
|
|
('DPM2 Karras', 'sample_dpm_2', ['k_dpm_2_ka'], {'scheduler': 'karras', 'discard_next_to_last_sigma': True, "uses_ensd": True, "second_order": True}),
|
|
('DPM2 a Karras', 'sample_dpm_2_ancestral', ['k_dpm_2_a_ka'], {'scheduler': 'karras', 'discard_next_to_last_sigma': True, "uses_ensd": True, "second_order": True}),
|
|
('DPM++ 2S a Karras', 'sample_dpmpp_2s_ancestral', ['k_dpmpp_2s_a_ka'], {'scheduler': 'karras', "uses_ensd": True, "second_order": True}),
|
|
('DPM++ 2M Karras', 'sample_dpmpp_2m', ['k_dpmpp_2m_ka'], {'scheduler': 'karras'}),
|
|
('DPM++ SDE Karras', 'sample_dpmpp_sde', ['k_dpmpp_sde_ka'], {'scheduler': 'karras', "second_order": True, "brownian_noise": True}),
|
|
('DPM++ 2M SDE Karras', 'sample_dpmpp_2m_sde', ['k_dpmpp_2m_sde_ka'], {'scheduler': 'karras', "brownian_noise": True}),
|
|
('Restart (new)', 'restart_sampler', ['restart'], {'scheduler': 'karras', "second_order": True}),
|
|
]
|
|
|
|
|
|
@torch.no_grad()
|
|
def restart_sampler(model, x, sigmas, extra_args=None, callback=None, disable=None, s_noise=1., restart_list = None):
|
|
"""Implements restart sampling in Restart Sampling for Improving Generative Processes (2023)"""
|
|
'''Restart_list format: {min_sigma: [ restart_steps, restart_times, max_sigma]}'''
|
|
'''If restart_list is None: will choose restart_list automatically, otherwise will use the given restart_list'''
|
|
from tqdm.auto import trange
|
|
extra_args = {} if extra_args is None else extra_args
|
|
s_in = x.new_ones([x.shape[0]])
|
|
step_id = 0
|
|
from k_diffusion.sampling import to_d, get_sigmas_karras
|
|
def heun_step(x, old_sigma, new_sigma, second_order = True):
|
|
nonlocal step_id
|
|
denoised = model(x, old_sigma * s_in, **extra_args)
|
|
d = to_d(x, old_sigma, denoised)
|
|
if callback is not None:
|
|
callback({'x': x, 'i': step_id, 'sigma': new_sigma, 'sigma_hat': old_sigma, 'denoised': denoised})
|
|
dt = new_sigma - old_sigma
|
|
if new_sigma == 0 or not second_order:
|
|
# Euler method
|
|
x = x + d * dt
|
|
else:
|
|
# Heun's method
|
|
x_2 = x + d * dt
|
|
denoised_2 = model(x_2, new_sigma * s_in, **extra_args)
|
|
d_2 = to_d(x_2, new_sigma, denoised_2)
|
|
d_prime = (d + d_2) / 2
|
|
x = x + d_prime * dt
|
|
step_id += 1
|
|
return x
|
|
steps = sigmas.shape[0] - 1
|
|
if restart_list is None:
|
|
if steps >= 20:
|
|
restart_steps = 9
|
|
restart_times = 2 if steps >= 36 else 1
|
|
sigmas = get_sigmas_karras(steps - restart_steps * restart_times, sigmas[-2].item(), sigmas[0].item(), device=sigmas.device)
|
|
restart_list = {0.1: [restart_steps + 1, restart_times, 2]}
|
|
else:
|
|
restart_list = dict()
|
|
temp_list = dict()
|
|
for key, value in restart_list.items():
|
|
temp_list[int(torch.argmin(abs(sigmas - key), dim=0))] = value
|
|
restart_list = temp_list
|
|
for i in trange(len(sigmas) - 1, disable=disable):
|
|
x = heun_step(x, sigmas[i], sigmas[i+1])
|
|
if i + 1 in restart_list:
|
|
restart_steps, restart_times, restart_max = restart_list[i + 1]
|
|
min_idx = i + 1
|
|
max_idx = int(torch.argmin(abs(sigmas - restart_max), dim=0))
|
|
if max_idx < min_idx:
|
|
sigma_restart = get_sigmas_karras(restart_steps, sigmas[min_idx].item(), sigmas[max_idx].item(), device=sigmas.device)[:-1] # remove the zero at the end
|
|
while restart_times > 0:
|
|
restart_times -= 1
|
|
x = x + torch.randn_like(x) * s_noise * (sigmas[max_idx] ** 2 - sigmas[min_idx] ** 2) ** 0.5
|
|
for (old_sigma, new_sigma) in zip(sigma_restart[:-1], sigma_restart[1:]):
|
|
x = heun_step(x, old_sigma, new_sigma)
|
|
return x
|
|
|
|
samplers_data_k_diffusion = [
|
|
sd_samplers_common.SamplerData(label, lambda model, funcname=funcname: KDiffusionSampler(funcname, model), aliases, options)
|
|
for label, funcname, aliases, options in samplers_k_diffusion
|
|
if (hasattr(k_diffusion.sampling, funcname) or funcname == 'restart_sampler')
|
|
]
|
|
|
|
sampler_extra_params = {
|
|
'sample_euler': ['s_churn', 's_tmin', 's_tmax', 's_noise'],
|
|
'sample_heun': ['s_churn', 's_tmin', 's_tmax', 's_noise'],
|
|
'sample_dpm_2': ['s_churn', 's_tmin', 's_tmax', 's_noise'],
|
|
}
|
|
|
|
k_diffusion_samplers_map = {x.name: x for x in samplers_data_k_diffusion}
|
|
k_diffusion_scheduler = {
|
|
'Automatic': None,
|
|
'karras': k_diffusion.sampling.get_sigmas_karras,
|
|
'exponential': k_diffusion.sampling.get_sigmas_exponential,
|
|
'polyexponential': k_diffusion.sampling.get_sigmas_polyexponential
|
|
}
|
|
|
|
|
|
class CFGDenoiser(torch.nn.Module):
|
|
"""
|
|
Classifier free guidance denoiser. A wrapper for stable diffusion model (specifically for unet)
|
|
that can take a noisy picture and produce a noise-free picture using two guidances (prompts)
|
|
instead of one. Originally, the second prompt is just an empty string, but we use non-empty
|
|
negative prompt.
|
|
"""
|
|
|
|
def __init__(self, model):
|
|
super().__init__()
|
|
self.inner_model = model
|
|
self.mask = None
|
|
self.nmask = None
|
|
self.init_latent = None
|
|
self.step = 0
|
|
self.image_cfg_scale = None
|
|
self.padded_cond_uncond = False
|
|
|
|
def combine_denoised(self, x_out, conds_list, uncond, cond_scale):
|
|
denoised_uncond = x_out[-uncond.shape[0]:]
|
|
denoised = torch.clone(denoised_uncond)
|
|
|
|
for i, conds in enumerate(conds_list):
|
|
for cond_index, weight in conds:
|
|
denoised[i] += (x_out[cond_index] - denoised_uncond[i]) * (weight * cond_scale)
|
|
|
|
return denoised
|
|
|
|
def combine_denoised_for_edit_model(self, x_out, cond_scale):
|
|
out_cond, out_img_cond, out_uncond = x_out.chunk(3)
|
|
denoised = out_uncond + cond_scale * (out_cond - out_img_cond) + self.image_cfg_scale * (out_img_cond - out_uncond)
|
|
|
|
return denoised
|
|
|
|
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
|
|
|
|
# at self.image_cfg_scale == 1.0 produced results for edit model are the same as with normal sampling,
|
|
# so is_edit_model is set to False to support AND composition.
|
|
is_edit_model = shared.sd_model.cond_stage_key == "edit" and self.image_cfg_scale is not None and self.image_cfg_scale != 1.0
|
|
|
|
conds_list, tensor = prompt_parser.reconstruct_multicond_batch(cond, self.step)
|
|
uncond = prompt_parser.reconstruct_cond_batch(uncond, self.step)
|
|
|
|
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)"
|
|
|
|
batch_size = len(conds_list)
|
|
repeats = [len(conds_list[i]) for i in range(batch_size)]
|
|
|
|
if shared.sd_model.model.conditioning_key == "crossattn-adm":
|
|
image_uncond = torch.zeros_like(image_cond)
|
|
make_condition_dict = lambda c_crossattn, c_adm: {"c_crossattn": c_crossattn, "c_adm": c_adm}
|
|
else:
|
|
image_uncond = image_cond
|
|
make_condition_dict = lambda c_crossattn, c_concat: {"c_crossattn": c_crossattn, "c_concat": [c_concat]}
|
|
|
|
if not is_edit_model:
|
|
x_in = torch.cat([torch.stack([x[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [x])
|
|
sigma_in = torch.cat([torch.stack([sigma[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [sigma])
|
|
image_cond_in = torch.cat([torch.stack([image_cond[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [image_uncond])
|
|
else:
|
|
x_in = torch.cat([torch.stack([x[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [x] + [x])
|
|
sigma_in = torch.cat([torch.stack([sigma[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [sigma] + [sigma])
|
|
image_cond_in = torch.cat([torch.stack([image_cond[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [image_uncond] + [torch.zeros_like(self.init_latent)])
|
|
|
|
denoiser_params = CFGDenoiserParams(x_in, image_cond_in, sigma_in, state.sampling_step, state.sampling_steps, tensor, uncond)
|
|
cfg_denoiser_callback(denoiser_params)
|
|
x_in = denoiser_params.x
|
|
image_cond_in = denoiser_params.image_cond
|
|
sigma_in = denoiser_params.sigma
|
|
tensor = denoiser_params.text_cond
|
|
uncond = denoiser_params.text_uncond
|
|
skip_uncond = False
|
|
|
|
# alternating uncond allows for higher thresholds without the quality loss normally expected from raising it
|
|
if self.step % 2 and s_min_uncond > 0 and sigma[0] < s_min_uncond and not is_edit_model:
|
|
skip_uncond = True
|
|
x_in = x_in[:-batch_size]
|
|
sigma_in = sigma_in[:-batch_size]
|
|
|
|
self.padded_cond_uncond = False
|
|
if shared.opts.pad_cond_uncond and tensor.shape[1] != uncond.shape[1]:
|
|
empty = shared.sd_model.cond_stage_model_empty_prompt
|
|
num_repeats = (tensor.shape[1] - uncond.shape[1]) // empty.shape[1]
|
|
|
|
if num_repeats < 0:
|
|
tensor = torch.cat([tensor, empty.repeat((tensor.shape[0], -num_repeats, 1))], axis=1)
|
|
self.padded_cond_uncond = True
|
|
elif num_repeats > 0:
|
|
uncond = torch.cat([uncond, empty.repeat((uncond.shape[0], num_repeats, 1))], axis=1)
|
|
self.padded_cond_uncond = True
|
|
|
|
if tensor.shape[1] == uncond.shape[1] or skip_uncond:
|
|
if is_edit_model:
|
|
cond_in = torch.cat([tensor, uncond, uncond])
|
|
elif skip_uncond:
|
|
cond_in = tensor
|
|
else:
|
|
cond_in = torch.cat([tensor, uncond])
|
|
|
|
if shared.batch_cond_uncond:
|
|
x_out = self.inner_model(x_in, sigma_in, cond=make_condition_dict([cond_in], image_cond_in))
|
|
else:
|
|
x_out = torch.zeros_like(x_in)
|
|
for batch_offset in range(0, x_out.shape[0], batch_size):
|
|
a = batch_offset
|
|
b = a + batch_size
|
|
x_out[a:b] = self.inner_model(x_in[a:b], sigma_in[a:b], cond=make_condition_dict([cond_in[a:b]], image_cond_in[a:b]))
|
|
else:
|
|
x_out = torch.zeros_like(x_in)
|
|
batch_size = batch_size*2 if shared.batch_cond_uncond else batch_size
|
|
for batch_offset in range(0, tensor.shape[0], batch_size):
|
|
a = batch_offset
|
|
b = min(a + batch_size, tensor.shape[0])
|
|
|
|
if not is_edit_model:
|
|
c_crossattn = [tensor[a:b]]
|
|
else:
|
|
c_crossattn = torch.cat([tensor[a:b]], uncond)
|
|
|
|
x_out[a:b] = self.inner_model(x_in[a:b], sigma_in[a:b], cond=make_condition_dict(c_crossattn, image_cond_in[a:b]))
|
|
|
|
if not skip_uncond:
|
|
x_out[-uncond.shape[0]:] = self.inner_model(x_in[-uncond.shape[0]:], sigma_in[-uncond.shape[0]:], cond=make_condition_dict([uncond], image_cond_in[-uncond.shape[0]:]))
|
|
|
|
denoised_image_indexes = [x[0][0] for x in conds_list]
|
|
if skip_uncond:
|
|
fake_uncond = torch.cat([x_out[i:i+1] for i in denoised_image_indexes])
|
|
x_out = torch.cat([x_out, fake_uncond]) # we skipped uncond denoising, so we put cond-denoised image to where the uncond-denoised image should be
|
|
|
|
denoised_params = CFGDenoisedParams(x_out, state.sampling_step, state.sampling_steps, self.inner_model)
|
|
cfg_denoised_callback(denoised_params)
|
|
|
|
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:
|
|
denoised = self.combine_denoised(x_out, conds_list, uncond, 1.0)
|
|
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
|
|
|
|
after_cfg_callback_params = AfterCFGCallbackParams(denoised, state.sampling_step, state.sampling_steps)
|
|
cfg_after_cfg_callback(after_cfg_callback_params)
|
|
denoised = after_cfg_callback_params.x
|
|
|
|
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
|
|
|
|
if opts.randn_source == "CPU" or x.device.type == 'mps':
|
|
return torch.randn_like(x, device=devices.cpu).to(x.device)
|
|
else:
|
|
return torch.randn_like(x)
|
|
|
|
|
|
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 = getattr(k_diffusion.sampling, self.funcname) if funcname != "restart_sampler" else restart_sampler
|
|
self.extra_params = sampler_extra_params.get(funcname, [])
|
|
self.model_wrap_cfg = 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
|
|
|
|
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):
|
|
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
|
|
|
|
return extra_params_kwargs
|
|
|
|
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)
|
|
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
|
|
|
|
if p.sampler_noise_scheduler_override:
|
|
sigmas = p.sampler_noise_scheduler_override(steps)
|
|
elif opts.k_sched_type != "Automatic":
|
|
m_sigma_min, m_sigma_max = (self.model_wrap.sigmas[0].item(), self.model_wrap.sigmas[-1].item())
|
|
sigma_min, sigma_max = (0.1, 10) if opts.use_old_karras_scheduler_sigmas else (m_sigma_min, m_sigma_max)
|
|
sigmas_kwargs = {
|
|
'sigma_min': sigma_min,
|
|
'sigma_max': sigma_max,
|
|
}
|
|
|
|
sigmas_func = k_diffusion_scheduler[opts.k_sched_type]
|
|
p.extra_generation_params["Schedule type"] = opts.k_sched_type
|
|
|
|
if opts.sigma_min != m_sigma_min and opts.sigma_min != 0:
|
|
sigmas_kwargs['sigma_min'] = opts.sigma_min
|
|
p.extra_generation_params["Schedule min sigma"] = opts.sigma_min
|
|
if opts.sigma_max != m_sigma_max and opts.sigma_max != 0:
|
|
sigmas_kwargs['sigma_max'] = opts.sigma_max
|
|
p.extra_generation_params["Schedule max sigma"] = opts.sigma_max
|
|
|
|
default_rho = 1. if opts.k_sched_type == "polyexponential" else 7.
|
|
|
|
if opts.k_sched_type != 'exponential' and opts.rho != 0 and opts.rho != default_rho:
|
|
sigmas_kwargs['rho'] = opts.rho
|
|
p.extra_generation_params["Schedule rho"] = opts.rho
|
|
|
|
sigmas = sigmas_func(n=steps, **sigmas_kwargs, device=shared.device)
|
|
elif self.config is not None and self.config.options.get('scheduler', None) == 'karras':
|
|
sigma_min, sigma_max = (0.1, 10) if opts.use_old_karras_scheduler_sigmas else (self.model_wrap.sigmas[0].item(), self.model_wrap.sigmas[-1].item())
|
|
|
|
sigmas = k_diffusion.sampling.get_sigmas_karras(n=steps, sigma_min=sigma_min, sigma_max=sigma_max, device=shared.device)
|
|
else:
|
|
sigmas = self.model_wrap.get_sigmas(steps)
|
|
|
|
if discard_next_to_last_sigma:
|
|
sigmas = torch.cat([sigmas[:-2], sigmas[-1:]])
|
|
|
|
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)
|
|
parameters = inspect.signature(self.func).parameters
|
|
|
|
if 'sigma_min' in parameters:
|
|
## last sigma is zero which isn't allowed by DPM Fast & Adaptive so taking value before last
|
|
extra_params_kwargs['sigma_min'] = sigma_sched[-2]
|
|
if 'sigma_max' in parameters:
|
|
extra_params_kwargs['sigma_max'] = sigma_sched[0]
|
|
if 'n' in parameters:
|
|
extra_params_kwargs['n'] = len(sigma_sched) - 1
|
|
if 'sigma_sched' in parameters:
|
|
extra_params_kwargs['sigma_sched'] = sigma_sched
|
|
if 'sigmas' in parameters:
|
|
extra_params_kwargs['sigmas'] = sigma_sched
|
|
|
|
if self.config.options.get('brownian_noise', False):
|
|
noise_sampler = self.create_noise_sampler(x, sigmas, p)
|
|
extra_params_kwargs['noise_sampler'] = noise_sampler
|
|
|
|
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
|
|
|
|
sigmas = self.get_sigmas(p, steps)
|
|
|
|
x = x * sigmas[0]
|
|
|
|
extra_params_kwargs = self.initialize(p)
|
|
parameters = inspect.signature(self.func).parameters
|
|
|
|
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:
|
|
extra_params_kwargs['sigmas'] = sigmas
|
|
|
|
if self.config.options.get('brownian_noise', False):
|
|
noise_sampler = self.create_noise_sampler(x, sigmas, p)
|
|
extra_params_kwargs['noise_sampler'] = noise_sampler
|
|
|
|
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
|
|
|