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import torch
from modules import sd_samplers_kdiffusion, sd_samplers_common
from ldm_patched.k_diffusion import sampling as k_diffusion_sampling
from ldm_patched.modules.samplers import calculate_sigmas
from modules import shared
ADAPTIVE_SOLVERS = {"dopri8", "dopri5", "bosh3", "fehlberg2", "adaptive_heun"}
FIXED_SOLVERS = {"euler", "midpoint", "rk4", "heun3", "explicit_adams", "implicit_adams"}
ALL_SOLVERS = list(ADAPTIVE_SOLVERS | FIXED_SOLVERS)
ALL_SOLVERS.sort()
class AlterSampler(sd_samplers_kdiffusion.KDiffusionSampler):
def __init__(self, sd_model, sampler_name, solver=None, rtol=None, atol=None):
self.sampler_name = sampler_name
self.scheduler_name = None
self.unet = sd_model.forge_objects.unet
self.model = sd_model
self.solver = solver
self.rtol = rtol
self.atol = atol
self.solver = solver
self.rtol = rtol
self.atol = atol
sampler_functions = {
'euler_comfy': k_diffusion_sampling.sample_euler,
'euler_ancestral_comfy': k_diffusion_sampling.sample_euler_ancestral,
'heun_comfy': k_diffusion_sampling.sample_heun,
'dpmpp_2s_ancestral_comfy': k_diffusion_sampling.sample_dpmpp_2s_ancestral,
'dpmpp_sde_comfy': k_diffusion_sampling.sample_dpmpp_sde,
'dpmpp_2m_comfy': k_diffusion_sampling.sample_dpmpp_2m,
'dpmpp_2m_sde_comfy': k_diffusion_sampling.sample_dpmpp_2m_sde,
'dpmpp_3m_sde_comfy': k_diffusion_sampling.sample_dpmpp_3m_sde,
'euler_ancestral_turbo': k_diffusion_sampling.sample_euler_ancestral,
'dpmpp_2m_turbo': k_diffusion_sampling.sample_dpmpp_2m,
'dpmpp_2m_sde_turbo': k_diffusion_sampling.sample_dpmpp_2m_sde,
'ddpm': k_diffusion_sampling.sample_ddpm,
'heunpp2': k_diffusion_sampling.sample_heunpp2,
'ipndm': k_diffusion_sampling.sample_ipndm,
'ipndm_v': k_diffusion_sampling.sample_ipndm_v,
'deis': k_diffusion_sampling.sample_deis,
'euler_cfg_pp': k_diffusion_sampling.sample_euler_cfg_pp,
'euler_ancestral_cfg_pp': k_diffusion_sampling.sample_euler_ancestral_cfg_pp,
'sample_euler_ancestral_RF': k_diffusion_sampling.sample_euler_ancestral_RF,
'dpmpp_2s_ancestral_cfg_pp': k_diffusion_sampling.sample_dpmpp_2s_ancestral_cfg_pp,
'sample_dpmpp_2s_ancestral_RF': k_diffusion_sampling.sample_dpmpp_2s_ancestral_RF,
'dpmpp_2s_ancestral_cfg_pp_dyn': k_diffusion_sampling.sample_dpmpp_2s_ancestral_cfg_pp_dyn,
'dpmpp_2s_ancestral_cfg_pp_intern': k_diffusion_sampling.sample_dpmpp_2s_ancestral_cfg_pp_intern,
'dpmpp_sde_cfg_pp': k_diffusion_sampling.sample_dpmpp_sde_cfg_pp,
'dpmpp_2m_cfg_pp': k_diffusion_sampling.sample_dpmpp_2m_cfg_pp,
'ode_bosh3': self.sample_ode_bosh3,
'ode_fehlberg2': self.sample_ode_fehlberg2,
'ode_adaptive_heun': self.sample_ode_adaptive_heun,
'ode_dopri5': self.sample_ode_dopri5,
'ode_custom':self.sample_ode_custom,
'dpmpp_3m_sde_cfg_pp': k_diffusion_sampling.sample_dpmpp_3m_sde_cfg_pp,
'dpmpp_2m_dy': k_diffusion_sampling.sample_dpmpp_2m_dy,
'dpmpp_3m_dy': k_diffusion_sampling.sample_dpmpp_3m_dy,
'dpmpp_3m_sde_dy': k_diffusion_sampling.sample_dpmpp_3m_sde_dy,
'euler_dy_cfg_pp': k_diffusion_sampling.sample_euler_dy_cfg_pp,
'euler_smea_dy_cfg_pp': k_diffusion_sampling.sample_euler_smea_dy_cfg_pp,
'euler_ancestral_dy_cfg_pp': k_diffusion_sampling.sample_euler_ancestral_dy_cfg_pp,
'dpmpp_2m_dy_cfg_pp': k_diffusion_sampling.sample_dpmpp_2m_dy_cfg_pp,
'clyb_4m_sde_momentumized': k_diffusion_sampling.sample_clyb_4m_sde_momentumized,
'res_solver': k_diffusion_sampling.sample_res_solver,
'kohaku_lonyu_yog_cfg_pp': k_diffusion_sampling.sample_kohaku_lonyu_yog_cfg_pp,
'custom_sampler': k_diffusion_sampling.sample_custom,
'res_multistep' : k_diffusion_sampling.sample_res_multistep,
'res_multistep_cfg_pp' : k_diffusion_sampling.sample_res_multistep_cfg_pp,
'gradient_estimation' : k_diffusion_sampling.sample_gradient_estimation,
'Kohaku_LoNyu_Yog' : k_diffusion_sampling.sample_Kohaku_LoNyu_Yog,
'ER SDE': k_diffusion_sampling.sample_er_sde,
}
sampler_function = sampler_functions.get(sampler_name)
if sampler_function is None:
raise ValueError(f"Unknown sampler: {sampler_name}")
super().__init__(sampler_function, sd_model, None)
def sample_func(self, model, x, sigmas, extra_args=None, callback=None, disable=None):
if self.sampler_name == 'ode_bosh3':
return self.sample_ode_bosh3(model, x, sigmas, extra_args, callback, disable)
elif self.sampler_name == 'ode_fehlberg2':
return self.sample_ode_fehlberg2(model, x, sigmas, extra_args, callback, disable)
elif self.sampler_name == 'ode_adaptive_heun':
return self.sample_ode_adaptive_heun(model, x, sigmas, extra_args, callback, disable)
elif self.sampler_name == 'ode_dopri5':
return self.sample_ode_dopri5(model, x, sigmas, extra_args, callback, disable)
elif self.sampler_name == 'ode_custom':
return self.sample_ode_custom(model, x, sigmas, extra_args, callback, disable)
else:
# For non-ODE samplers, use the original sampler function
return super().sample_func(model, x, sigmas, extra_args, callback, disable)
def sample_ode_bosh3(self, model, x, sigmas, extra_args=None, callback=None, disable=None):
return k_diffusion_sampling.sample_ode(model, x, sigmas, extra_args=extra_args, callback=callback, disable=disable,
solver="bosh3",
rtol=10**shared.opts.ode_bosh3_rtol,
atol=10**shared.opts.ode_bosh3_atol,
max_steps=shared.opts.ode_bosh3_max_steps)
def sample_ode_fehlberg2(self, model, x, sigmas, extra_args=None, callback=None, disable=None):
return k_diffusion_sampling.sample_ode(model, x, sigmas, extra_args=extra_args, callback=callback, disable=disable,
solver="fehlberg2",
rtol=10**shared.opts.ode_fehlberg2_rtol,
atol=10**shared.opts.ode_fehlberg2_atol,
max_steps=shared.opts.ode_fehlberg2_max_steps)
def sample_ode_adaptive_heun(self, model, x, sigmas, extra_args=None, callback=None, disable=None):
return k_diffusion_sampling.sample_ode(model, x, sigmas, extra_args=extra_args, callback=callback, disable=disable,
solver="adaptive_heun",
rtol=10**shared.opts.ode_adaptive_heun_rtol,
atol=10**shared.opts.ode_adaptive_heun_atol,
max_steps=shared.opts.ode_adaptive_heun_max_steps)
def sample_ode_dopri5(self, model, x, sigmas, extra_args=None, callback=None, disable=None):
return k_diffusion_sampling.sample_ode(model, x, sigmas, extra_args=extra_args, callback=callback, disable=disable,
solver="dopri5",
rtol=10**shared.opts.ode_dopri5_rtol,
atol=10**shared.opts.ode_dopri5_atol,
max_steps=shared.opts.ode_dopri5_max_steps)
def sample_ode_custom(self, model, x, sigmas, extra_args=None, callback=None, disable=None):
solver = shared.opts.ode_custom_solver
rtol = 10**shared.opts.ode_custom_rtol if solver in ADAPTIVE_SOLVERS else None
atol = 10**shared.opts.ode_custom_atol if solver in ADAPTIVE_SOLVERS else None
max_steps = shared.opts.ode_custom_max_steps
return k_diffusion_sampling.sample_ode(model, x, sigmas, extra_args=extra_args, callback=callback, disable=disable,
solver=solver, rtol=rtol, atol=atol, max_steps=max_steps)
def sample(self, p, x, conditioning, unconditional_conditioning, steps=None, image_conditioning=None):
self.scheduler_name = p.scheduler
return super().sample(p, x, conditioning, unconditional_conditioning, steps, image_conditioning)
def sample_img2img(self, p, x, noise, conditioning, unconditional_conditioning, steps=None, image_conditioning=None):
self.scheduler_name = p.scheduler
return super().sample_img2img(p, x, noise, conditioning, unconditional_conditioning, steps, image_conditioning)
def get_sigmas(self, p, steps):
if self.scheduler_name is None:
self.scheduler_name = 'Normal' # Default to 'Normal' if not set
forge_schedulers = {
"Normal": "normal",
"Karras": "karras",
"Exponential": "exponential",
"SGM Uniform": "sgm_uniform",
"Simple": "simple",
"DDIM": "ddim_uniform",
"Align Your Steps": "ays",
"Align Your Steps GITS": "ays_gits",
"Align Your Steps 11": "ays_11steps",
"Align Your Steps 32": "ays_32steps",
"KL Optimal": "kl_optimal",
"Beta": "beta",
"Sinusoidal SF": "sinusoidal_sf",
"Invcosinusoidal SF": "invcosinusoidal_sf",
"React Cosinusoidal DynSF": "react_cosinusoidal_dynsf",
"Cosine": "cosine",
"Cosine-exponential Blend": "cosexpblend",
"Phi": "phi",
"Laplace": "laplace",
"Karras Dynamic": "karras_dynamic",
"Uniform": "uniform",
"Polyexponential": "polyexponential",
"Turbo": "turbo",
"Align Your Steps Custom": "ays_custom",
}
use_turbo = self.sampler_name.endswith('_turbo') or self.scheduler_name.lower() == "turbo".lower()
forge_schedulers_lower = {k.lower(): v for k, v in forge_schedulers.items()}
scheduler_key_lower = self.scheduler_name.lower() if self.scheduler_name else ""
if scheduler_key_lower in forge_schedulers_lower:
matched_scheduler = forge_schedulers_lower[scheduler_key_lower]
else:
# Default to 'normal' if not available
matched_scheduler = 'normal'
try:
if use_turbo:
# Use Turbo scheduler
timesteps = torch.flip(torch.arange(1, steps + 1) * float(1000.0 / steps) - 1, (0,)).round().long().clip(0, 999)
sigmas = self.unet.model.model_sampling.sigma(timesteps)
sigmas = torch.cat([sigmas, sigmas.new_zeros([1])])
else:
sigmas = calculate_sigmas(self.unet.model.model_sampling, matched_scheduler, steps, is_sdxl=getattr(self.model, "is_sdxl", False))
except Exception as e:
print(f"Error calculating sigmas for scheduler {matched_scheduler}: {str(e)}")
print("Falling back to normal scheduler")
sigmas = calculate_sigmas(self.unet.model.model_sampling, "normal", steps, is_sdxl=getattr(self.model, "is_sdxl", False))
if sigmas is None:
raise ValueError(f"Invalid scheduler: {self.scheduler_name}")
return sigmas.to(self.unet.load_device)
def build_constructor(sampler_name):
def constructor(model):
return AlterSampler(model, sampler_name)
return constructor
samplers_data_alter = [
sd_samplers_common.SamplerData('ER SDE', build_constructor(sampler_name='ER SDE'), ['ER SDE'], {}),
sd_samplers_common.SamplerData('Kohaku_LoNyu_Yog', build_constructor(sampler_name='Kohaku_LoNyu_Yog'), ['Kohaku_LoNyu_Yog'], {}),
sd_samplers_common.SamplerData('Euler CFG++', build_constructor(sampler_name='euler_cfg_pp'), ['euler_cfg_pp'], {}),
sd_samplers_common.SamplerData('Euler Ancestral CFG++', build_constructor(sampler_name='euler_ancestral_cfg_pp'), ['euler_ancestral_cfg_pp'], {}),
sd_samplers_common.SamplerData('DPM++ 2S Ancestral CFG++', build_constructor(sampler_name='dpmpp_2s_ancestral_cfg_pp'), ['dpmpp_2s_ancestral_cfg_pp'], {}),
sd_samplers_common.SamplerData('DPM++ 2M CFG++', build_constructor(sampler_name='dpmpp_2m_cfg_pp'), ['dpmpp_2m_cfg_pp'], {}),
sd_samplers_common.SamplerData('DPM++ SDE CFG++', build_constructor(sampler_name='dpmpp_sde_cfg_pp'), ['dpmpp_sde_cfg_pp'], {}),
sd_samplers_common.SamplerData('DPM++ 3M SDE CFG++', build_constructor(sampler_name='dpmpp_3m_sde_cfg_pp'), ['dpmpp_3m_sde_cfg_pp'], {}),
sd_samplers_common.SamplerData('Euler DY CFG++', build_constructor(sampler_name='euler_dy_cfg_pp'), ['euler_dy_cfg_pp'], {}),
sd_samplers_common.SamplerData('Euler SMEA DY CFG++', build_constructor(sampler_name='euler_smea_dy_cfg_pp'), ['euler_smea_dy_cfg_pp'], {}),
sd_samplers_common.SamplerData('Euler A DY CFG++', build_constructor(sampler_name='euler_ancestral_dy_cfg_pp'), ['euler_ancestral_dy_cfg_pp'], {}),
sd_samplers_common.SamplerData('DPM++ 2M DY CFG++', build_constructor(sampler_name='dpmpp_2m_dy_cfg_pp'), ['dpmpp_2m_dy_cfg_pp'], {}),
sd_samplers_common.SamplerData('RES Multistep', build_constructor(sampler_name='res_multistep'), ['res_multistep'], {}),
sd_samplers_common.SamplerData('RES Multistep CFG++', build_constructor(sampler_name='res_multistep_cfg_pp'), ['res_multistep_cfg_pp'], {}),
sd_samplers_common.SamplerData('Gradient Estimation', build_constructor(sampler_name='gradient_estimation'), ['gradient_estimation'], {}),
# sd_samplers_common.SamplerData('Kohaku_LoNyu_Yog CFG++', build_constructor(sampler_name='kohaku_lonyu_yog_cfg_pp'), ['kohaku_lonyu_yog_cfg_pp'], {}),
sd_samplers_common.SamplerData('DPM++ 2M DY', build_constructor(sampler_name='dpmpp_2m_dy'), ['dpmpp_2m_dy'], {}),
sd_samplers_common.SamplerData('DPM++ 3M DY', build_constructor(sampler_name='dpmpp_3m_dy'), ['dpmpp_3m_dy'], {}),
sd_samplers_common.SamplerData('DPM++ 3M SDE DY', build_constructor(sampler_name='dpmpp_3m_sde_dy'), ['dpmpp_3m_sde_dy'], {}),
sd_samplers_common.SamplerData('DDPM', build_constructor(sampler_name='ddpm'), ['ddpm'], {}),
sd_samplers_common.SamplerData('HeunPP2', build_constructor(sampler_name='heunpp2'), ['heunpp2'], {}),
sd_samplers_common.SamplerData('IPNDM', build_constructor(sampler_name='ipndm'), ['ipndm'], {}),
sd_samplers_common.SamplerData('IPNDM_V', build_constructor(sampler_name='ipndm_v'), ['ipndm_v'], {}),
sd_samplers_common.SamplerData('DEIS', build_constructor(sampler_name='deis'), ['deis'], {}),
sd_samplers_common.SamplerData('RES Solver', build_constructor(sampler_name='res_solver'), ['res_solver'], {}),
sd_samplers_common.SamplerData('ODE (Bosh3)', build_constructor(sampler_name='ode_bosh3'), ['ode_bosh3'], {}),
sd_samplers_common.SamplerData('ODE (Fehlberg2)', build_constructor(sampler_name='ode_fehlberg2'), ['ode_fehlberg2'], {}),
sd_samplers_common.SamplerData('ODE (Adaptive Heun)', build_constructor(sampler_name='ode_adaptive_heun'), ['ode_adaptive_heun'], {}),
sd_samplers_common.SamplerData('ODE (Dopri5)', build_constructor(sampler_name='ode_dopri5'), ['ode_dopri5'], {}),
sd_samplers_common.SamplerData('ODE Custom', build_constructor(sampler_name='ode_custom'), ['ode_custom'], {}),
sd_samplers_common.SamplerData('Euler A Turbo', build_constructor(sampler_name='euler_ancestral_turbo'), ['euler_ancestral_turbo'], {}),
sd_samplers_common.SamplerData('DPM++ 2M Turbo', build_constructor(sampler_name='dpmpp_2m_turbo'), ['dpmpp_2m_turbo'], {}),
sd_samplers_common.SamplerData('DPM++ 2M SDE Turbo', build_constructor(sampler_name='dpmpp_2m_sde_turbo'), ['dpmpp_2m_sde_turbo'], {}),
sd_samplers_common.SamplerData('Euler Comfy', build_constructor(sampler_name='euler_comfy'), ['euler_comfy'], {}),
sd_samplers_common.SamplerData('Euler A Comfy', build_constructor(sampler_name='euler_ancestral_comfy'), ['euler_ancestral_comfy'], {}),
sd_samplers_common.SamplerData('Heun Comfy', build_constructor(sampler_name='heun_comfy'), ['heun_comfy'], {}),
sd_samplers_common.SamplerData('DPM++ 2S Ancestral Comfy', build_constructor(sampler_name='dpmpp_2s_ancestral_comfy'), ['dpmpp_2s_ancestral_comfy'], {}),
sd_samplers_common.SamplerData('DPM++ SDE Comfy', build_constructor(sampler_name='dpmpp_sde_comfy'), ['dpmpp_sde_comfy'], {}),
sd_samplers_common.SamplerData('DPM++ 2M Comfy', build_constructor(sampler_name='dpmpp_2m_comfy'), ['dpmpp_2m_comfy'], {}),
sd_samplers_common.SamplerData('DPM++ 2M SDE Comfy', build_constructor(sampler_name='dpmpp_2m_sde_comfy'), ['dpmpp_2m_sde_comfy'], {}),
sd_samplers_common.SamplerData('DPM++ 3M SDE Comfy', build_constructor(sampler_name='dpmpp_3m_sde_comfy'), ['dpmpp_3m_sde_comfy'], {}),
# sd_samplers_common.SamplerData('CLYB 4M SDE Momentumized', build_constructor(sampler_name='clyb_4m_sde_momentumized'), ['clyb_4m_sde_momentumized'], {}),
sd_samplers_common.SamplerData('DPM++ 2S Ancestral CFG++ Dyn', build_constructor(sampler_name='dpmpp_2s_ancestral_cfg_pp_dyn'), ['dpmpp_2s_ancestral_cfg_pp_dyn'], {}),
sd_samplers_common.SamplerData('DPM++ 2S Ancestral CFG++ Intern', build_constructor(sampler_name='dpmpp_2s_ancestral_cfg_pp_intern'), ['dpmpp_2s_ancestral_cfg_pp_intern'], {}),
sd_samplers_common.SamplerData('Custom Sampler', build_constructor(sampler_name='custom_sampler'), ['custom_sampler'], {}),
#sd_samplers_common.SamplerData('Euler A RF', build_constructor(sampler_name='sample_euler_ancestral_RF'), ['sample_euler_ancestral_RF'], {}),
# sd_samplers_common.SamplerData('DPM++ 2S Ancestral RF', build_constructor(sampler_name='sample_dpmpp_2s_ancestral_RF'), ['sample_dpmpp_2s_ancestral_RF'], {}),
]
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