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Running
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Zero
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import threading
import torch
from tqdm.auto import trange
from modules.Utilities import util
from modules.sample import sampling_util
disable_gui = False
@torch.no_grad()
def sample_euler_ancestral(
model,
x,
sigmas,
extra_args=None,
callback=None,
disable=None,
eta=1.0,
s_noise=1.0,
noise_sampler=None,
pipeline=False,
):
# Pre-calculate common values
device = x.device
global disable_gui
disable_gui = pipeline
if not disable_gui:
from modules.AutoEncoders import taesd
from modules.user import app_instance
# Pre-allocate tensors and init noise sampler
s_in = torch.ones((x.shape[0],), device=device)
noise_sampler = (
sampling_util.default_noise_sampler(x)
if noise_sampler is None
else noise_sampler
)
for i in trange(len(sigmas) - 1, disable=disable):
if (
not pipeline
and hasattr(app_instance.app, "interrupt_flag")
and app_instance.app.interrupt_flag
):
return x
if not pipeline:
app_instance.app.progress.set(i / (len(sigmas) - 1))
# Combined model inference and step calculation
denoised = model(x, sigmas[i] * s_in, **(extra_args or {}))
sigma_down, sigma_up = sampling_util.get_ancestral_step(
sigmas[i], sigmas[i + 1], eta=eta
)
# Fused update step
x = x + util.to_d(x, sigmas[i], denoised) * (sigma_down - sigmas[i])
if sigmas[i + 1] > 0:
x = x + noise_sampler(sigmas[i], sigmas[i + 1]) * s_noise * sigma_up
if callback is not None:
callback({"x": x, "i": i, "sigma": sigmas[i], "denoised": denoised})
if not pipeline and app_instance.app.previewer_var.get() and i % 5 == 0:
threading.Thread(target=taesd.taesd_preview, args=(x,)).start()
return x
@torch.no_grad()
def sample_euler(
model,
x,
sigmas,
extra_args=None,
callback=None,
disable=None,
s_churn=0.0,
s_tmin=0.0,
s_tmax=float("inf"),
s_noise=1.0,
pipeline=False,
):
# Pre-calculate common values
device = x.device
global disable_gui
disable_gui = pipeline
if not disable_gui:
from modules.AutoEncoders import taesd
from modules.user import app_instance
# Pre-allocate tensors and cache parameters
s_in = torch.ones((x.shape[0],), device=device)
gamma_max = min(s_churn / (len(sigmas) - 1), 2**0.5 - 1) if s_churn > 0 else 0
for i in trange(len(sigmas) - 1, disable=disable):
if (
not pipeline
and hasattr(app_instance.app, "interrupt_flag")
and app_instance.app.interrupt_flag
):
return x
if not pipeline:
app_instance.app.progress.set(i / (len(sigmas) - 1))
# Combined sigma calculation and update
sigma_hat = (
sigmas[i] * (1 + (gamma_max if s_tmin <= sigmas[i] <= s_tmax else 0))
if gamma_max > 0
else sigmas[i]
)
if gamma_max > 0 and sigma_hat > sigmas[i]:
x = (
x
+ torch.randn_like(x) * s_noise * (sigma_hat**2 - sigmas[i] ** 2) ** 0.5
)
# Fused model inference and update step
denoised = model(x, sigma_hat * s_in, **(extra_args or {}))
x = x + util.to_d(x, sigma_hat, denoised) * (sigmas[i + 1] - sigma_hat)
if callback is not None:
callback(
{
"x": x,
"i": i,
"sigma": sigmas[i],
"sigma_hat": sigma_hat,
"denoised": denoised,
}
)
if not pipeline and app_instance.app.previewer_var.get() and i % 5 == 0:
threading.Thread(target=taesd.taesd_preview, args=(x, True)).start()
return x
def set_model_options_post_cfg_function(
model_options, post_cfg_function, disable_cfg1_optimization=False
):
model_options["sampler_post_cfg_function"] = model_options.get(
"sampler_post_cfg_function", []
) + [post_cfg_function]
if disable_cfg1_optimization:
model_options["disable_cfg1_optimization"] = True
return model_options
@torch.no_grad()
def sample_dpmpp_2m_cfgpp(
model,
x,
sigmas,
extra_args=None,
callback=None,
disable=None,
pipeline=False,
# CFG++ parameters
cfg_scale=7.5,
cfg_x0_scale=1.0,
cfg_s_scale=1.0,
cfg_min=1.0,
):
"""DPM-Solver++(2M) sampler with CFG++ optimizations"""
# Pre-calculate common values and setup
device = x.device
global disable_gui
disable_gui = pipeline
if not disable_gui:
from modules.AutoEncoders import taesd
from modules.user import app_instance
# Pre-allocate tensors and transform sigmas
s_in = torch.ones((x.shape[0],), device=device)
t_steps = -torch.log(sigmas) # Fused calculation
n_steps = len(sigmas) - 1
# Pre-calculate all needed values in one go
sigma_steps = torch.exp(-t_steps) # Fused calculation
ratios = sigma_steps[1:] / sigma_steps[:-1]
h_steps = t_steps[1:] - t_steps[:-1]
# Pre-calculate CFG schedule for the entire sampling process
steps = torch.arange(n_steps, device=device)
cfg_values = cfg_scale + (cfg_min - cfg_scale) * (steps / n_steps)
old_denoised = None
old_uncond_denoised = None
extra_args = {} if extra_args is None else extra_args
# Define post-CFG function once outside the loop
def post_cfg_function(args):
nonlocal old_uncond_denoised
old_uncond_denoised = args["uncond_denoised"]
return args["denoised"]
model_options = extra_args.get("model_options", {}).copy()
extra_args["model_options"] = set_model_options_post_cfg_function(
model_options, post_cfg_function, disable_cfg1_optimization=True
)
for i in trange(n_steps, disable=disable):
if (
not pipeline
and hasattr(app_instance.app, "interrupt_flag")
and app_instance.app.interrupt_flag
):
return x
if not pipeline:
app_instance.app.progress.set(i / n_steps)
# Use pre-calculated CFG scale
current_cfg = cfg_values[i]
# Fused model inference and update calculations
denoised = model(x, sigmas[i] * s_in, **extra_args)
uncond_denoised = extra_args.get("model_options", {}).get(
"sampler_post_cfg_function", []
)[-1]({"denoised": denoised, "uncond_denoised": None})
if callback is not None:
callback(
{
"x": x,
"i": i,
"sigma": sigmas[i],
"sigma_hat": sigmas[i],
"denoised": denoised,
"cfg_scale": current_cfg,
}
)
# CFG++ update step using optimized operations
if old_uncond_denoised is None or sigmas[i + 1] == 0:
# First step or last step - use torch.lerp for efficient interpolation
cfg_denoised = torch.lerp(uncond_denoised, denoised, current_cfg)
else:
# Fused momentum calculations
h_ratio = h_steps[i - 1] / (2 * h_steps[i])
h_ratio_plus_1 = 1 + h_ratio
# Use fused multiply-add operations for momentum terms
momentum = torch.addcmul(denoised * h_ratio_plus_1, old_denoised, -h_ratio)
uncond_momentum = torch.addcmul(
uncond_denoised * h_ratio_plus_1, old_uncond_denoised, -h_ratio
)
# Optimized interpolation for CFG++ update
cfg_denoised = torch.lerp(
uncond_momentum, momentum, current_cfg * cfg_x0_scale
)
# Apply update with pre-calculated expm1
h_expm1 = torch.expm1(-h_steps[i])
x = ratios[i] * x - h_expm1 * cfg_denoised
old_denoised = denoised
old_uncond_denoised = uncond_denoised
# Preview updates
if not pipeline and app_instance.app.previewer_var.get() and i % 5 == 0:
threading.Thread(target=taesd.taesd_preview, args=(x,)).start()
return x
@torch.no_grad()
def sample_dpmpp_sde_cfgpp(
model,
x,
sigmas,
extra_args=None,
callback=None,
disable=None,
eta=1.0,
s_noise=1.0,
noise_sampler=None,
r=1 / 2,
pipeline=False,
seed=None,
# CFG++ parameters
cfg_scale=7.5,
cfg_x0_scale=1.0,
cfg_s_scale=1.0,
cfg_min=1.0,
):
"""DPM-Solver++ (SDE) with CFG++ optimizations"""
# Pre-calculate common values
device = x.device
global disable_gui
disable_gui = pipeline
if not disable_gui:
from modules.AutoEncoders import taesd
from modules.user import app_instance
# Early return check
if len(sigmas) <= 1:
return x
# Pre-allocate tensors and values
s_in = torch.ones((x.shape[0],), device=device)
n_steps = len(sigmas) - 1
extra_args = {} if extra_args is None else extra_args
# CFG++ scheduling
def get_cfg_scale(step):
progress = step / n_steps
return cfg_scale + (cfg_min - cfg_scale) * progress
# Helper functions
def sigma_fn(t):
return (-t).exp()
def t_fn(sigma):
return -sigma.log()
# Initialize noise sampler
if noise_sampler is None:
noise_sampler = sampling_util.BrownianTreeNoiseSampler(
x, sigmas[sigmas > 0].min(), sigmas.max(), seed=seed, cpu=True
)
# Track previous predictions
old_denoised = None
old_uncond_denoised = None
def post_cfg_function(args):
nonlocal old_uncond_denoised
old_uncond_denoised = args["uncond_denoised"]
return args["denoised"]
model_options = extra_args.get("model_options", {}).copy()
extra_args["model_options"] = set_model_options_post_cfg_function(
model_options, post_cfg_function, disable_cfg1_optimization=True
)
for i in trange(n_steps, disable=disable):
if (
not pipeline
and hasattr(app_instance.app, "interrupt_flag")
and app_instance.app.interrupt_flag
):
return x
if not pipeline:
app_instance.app.progress.set(i / n_steps)
# Get current CFG scale
current_cfg = get_cfg_scale(i)
# Model inference
denoised = model(x, sigmas[i] * s_in, **extra_args)
uncond_denoised = extra_args.get("model_options", {}).get(
"sampler_post_cfg_function", []
)[-1]({"denoised": denoised, "uncond_denoised": None})
if callback is not None:
callback(
{
"x": x,
"i": i,
"sigma": sigmas[i],
"denoised": denoised,
"cfg_scale": current_cfg,
}
)
if sigmas[i + 1] == 0:
# Final step - regular CFG
cfg_denoised = uncond_denoised + (denoised - uncond_denoised) * current_cfg
x = x + util.to_d(x, sigmas[i], cfg_denoised) * (sigmas[i + 1] - sigmas[i])
else:
# Two-step update with CFG++
t, t_next = t_fn(sigmas[i]), t_fn(sigmas[i + 1])
s = t + (t_next - t) * r
# Step 1 with CFG++
sd, su = sampling_util.get_ancestral_step(sigma_fn(t), sigma_fn(s), eta)
s_ = t_fn(sd)
if old_uncond_denoised is None:
# First step - regular CFG
cfg_denoised = (
uncond_denoised + (denoised - uncond_denoised) * current_cfg
)
else:
# CFG++ with momentum
x0_coeff = cfg_x0_scale * current_cfg
# Calculate momentum terms
h_ratio = (t - s_) / (2 * (t - t_next))
momentum = (1 + h_ratio) * denoised - h_ratio * old_denoised
uncond_momentum = (
1 + h_ratio
) * uncond_denoised - h_ratio * old_uncond_denoised
# Combine with CFG++ scaling
cfg_denoised = uncond_momentum + (momentum - uncond_momentum) * x0_coeff
x_2 = (
(sigma_fn(s_) / sigma_fn(t)) * x
- (t - s_).expm1() * cfg_denoised
+ noise_sampler(sigma_fn(t), sigma_fn(s)) * s_noise * su
)
# Step 2 inference
denoised_2 = model(x_2, sigma_fn(s) * s_in, **extra_args)
uncond_denoised_2 = extra_args.get("model_options", {}).get(
"sampler_post_cfg_function", []
)[-1]({"denoised": denoised_2, "uncond_denoised": None})
# Step 2 CFG++ combination
if old_uncond_denoised is None:
cfg_denoised_2 = (
uncond_denoised_2 + (denoised_2 - uncond_denoised_2) * current_cfg
)
else:
momentum_2 = (1 + h_ratio) * denoised_2 - h_ratio * denoised
uncond_momentum_2 = (
1 + h_ratio
) * uncond_denoised_2 - h_ratio * uncond_denoised
cfg_denoised_2 = (
uncond_momentum_2 + (momentum_2 - uncond_momentum_2) * x0_coeff
)
# Final ancestral step
sd, su = sampling_util.get_ancestral_step(
sigma_fn(t), sigma_fn(t_next), eta
)
t_next_ = t_fn(sd)
# Combined update with both predictions
x = (
(sigma_fn(t_next_) / sigma_fn(t)) * x
- (t - t_next_).expm1()
* ((1 - 1 / (2 * r)) * cfg_denoised + (1 / (2 * r)) * cfg_denoised_2)
+ noise_sampler(sigma_fn(t), sigma_fn(t_next)) * s_noise * su
)
old_denoised = denoised
old_uncond_denoised = uncond_denoised
# Preview updates
if not pipeline and app_instance.app.previewer_var.get() and i % 5 == 0:
threading.Thread(target=taesd.taesd_preview, args=(x,)).start()
return x
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