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legacy_samplers.py
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legacy_samplers.py
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from .noise_classes import *
from .sigmas import get_sigmas
import comfy.samplers
import comfy.sample
import comfy.sampler_helpers
import comfy.model_sampling
import comfy.latent_formats
import comfy.sd
from comfy_extras.nodes_model_advanced import ModelSamplingSD3, ModelSamplingFlux, ModelSamplingAuraFlow, ModelSamplingStableCascade
import comfy.supported_models
import latent_preview
import torch
import torch.nn.functional as F
from .helper import get_res4lyf_scheduler_list
def initialize_or_scale(tensor, value, steps):
if tensor is None:
return torch.full((steps,), value)
else:
return value * tensor
def move_to_same_device(*tensors):
if not tensors:
return tensors
device = tensors[0].device
return tuple(tensor.to(device) for tensor in tensors)
RK_SAMPLER_NAMES = ["res_2m",
"res_3m",
"res_2s",
"res_3s",
"rk_exp_5s",
"deis_2m",
"deis_3m",
"deis_4m",
"ralston_2s",
"ralston_3s",
"ralston_4s",
"dpmpp_2m",
"dpmpp_3m",
"dpmpp_2s",
"dpmpp_sde_2s",
"dpmpp_3s",
"midpoint_2s",
"heun_2s",
"heun_3s",
"houwen-wray_3s",
"kutta_3s",
"ssprk3_3s",
"rk38_4s",
"rk4_4s",
"dormand-prince_6s",
"dormand-prince_13s",
"bogacki-shampine_7s",
"ddim",
"euler",
]
IRK_SAMPLER_NAMES = [
"gauss-legendre_2s",
"gauss-legendre_3s",
"gauss-legendre_4s",
"gauss-legendre_5s",
"radau_iia_2s",
"radau_iia_3s",
"lobatto_iiic_2s",
"lobatto_iiic_3s",
"crouzeix_2s",
"crouzeix_3s",
"irk_exp_diag_2s",
"use_explicit",
]
class Legacy_ClownsharKSampler:
@classmethod
def INPUT_TYPES(s):
return {"required":
{"model": ("MODEL",),
#"add_noise": ("BOOLEAN", {"default": True}),
"noise_type_init": (NOISE_GENERATOR_NAMES_SIMPLE, {"default": "gaussian"}),
"noise_type_sde": (NOISE_GENERATOR_NAMES_SIMPLE, {"default": "brownian"}),
"noise_mode_sde": (["hard", "hard_var", "hard_sq", "soft", "softer", "exp"], {"default": 'hard', "tooltip": "How noise scales with the sigma schedule. Hard is the most aggressive, the others start strong and drop rapidly."}),
"eta": ("FLOAT", {"default": 0.25, "min": -100.0, "max": 100.0, "step":0.01, "round": False, "tooltip": "Calculated noise amount to be added, then removed, after each step."}),
"noise_seed": ("INT", {"default": 0, "min": -1, "max": 0xffffffffffffffff}),
#"sampler_mode": (['standard', 'unsample', 'resample'],),
"sampler_mode": (['standard', 'unsample', 'resample',],),
"sampler_name": (RK_SAMPLER_NAMES, {"default": "res_2m"}),
"implicit_sampler_name": (["default",
"gauss-legendre_5s",
"gauss-legendre_4s",
"gauss-legendre_3s",
"gauss-legendre_2s",
"crouzeix_2s",
"radau_iia_3s",
"radau_iia_2s",
"lobatto_iiic_3s",
"lobatto_iiic_2s",
], {"default": "default"}),
"scheduler": (get_res4lyf_scheduler_list(), {"default": "beta57"},),
"steps": ("INT", {"default": 30, "min": 1, "max": 10000}),
"implicit_steps": ("INT", {"default": 0, "min": 0, "max": 10000}),
"denoise": ("FLOAT", {"default": 1.0, "min": -10000, "max": 10000, "step":0.01}),
"denoise_alt": ("FLOAT", {"default": 1.0, "min": -10000, "max": 10000, "step":0.01}),
"cfg": ("FLOAT", {"default": 5.0, "min": -100.0, "max": 100.0, "step":0.1, "round": False, }),
"shift": ("FLOAT", {"default": 3.0, "min": -1.0, "max": 100.0, "step":0.1, "round": False, }),
"base_shift": ("FLOAT", {"default": 0.85, "min": -1.0, "max": 100.0, "step":0.1, "round": False, }),
"truncate_conditioning": (['false', 'true'], {"default": "true"}),
},
"optional":
{
"positive": ("CONDITIONING", ),
"negative": ("CONDITIONING", ),
"sigmas": ("SIGMAS", ),
"latent_image": ("LATENT", ),
"guides": ("GUIDES", ),
"options": ("OPTIONS", ),
}
}
RETURN_TYPES = ("LATENT","LATENT", ) #"LATENT","LATENT")
RETURN_NAMES = ("output", "denoised",) # "output_fp64", "denoised_fp64")
FUNCTION = "main"
CATEGORY = "RES4LYF/legacy/samplers"
def main(self, model, cfg, truncate_conditioning, sampler_mode, scheduler, steps, denoise=1.0, denoise_alt=1.0,
noise_type_init="gaussian", noise_type_sde="brownian", noise_mode_sde="hard", latent_image=None,
positive=None, negative=None, sigmas=None, latent_noise=None, latent_noise_match=None,
noise_stdev=1.0, noise_mean=0.0, noise_normalize=True, noise_is_latent=False,
eta=0.25, eta_var=0.0, d_noise=1.0, s_noise=1.0, alpha_init=-1.0, k_init=1.0, alpha_sde=-1.0, k_sde=1.0, cfgpp=0.0, c1=0.0, c2=0.5, c3=1.0, multistep=False, noise_seed=-1, sampler_name="res_2m", implicit_sampler_name="default",
exp_mode=False, t_fn_formula=None, sigma_fn_formula=None, implicit_steps=0,
latent_guide=None, latent_guide_inv=None, latent_guide_weight=0.0, guide_mode="blend", latent_guide_weights=None, latent_guide_mask=None, rescale_floor=True, sigmas_override=None, unsampler_type="linear",
shift=3.0, base_shift=0.85, guides=None, options=None,
):
default_dtype = torch.float64
max_steps = 10000
if noise_seed == -1:
seed = torch.initial_seed() + 1
else:
seed = noise_seed
torch.manual_seed(noise_seed)
noise_seed_sde = seed + 1
if options is not None:
noise_stdev = options.get('noise_init_stdev', noise_stdev)
noise_mean = options.get('noise_init_mean', noise_mean)
noise_type_init = options.get('noise_type_init', noise_type_init)
noise_type_sde = options.get('noise_type_sde', noise_type_sde)
noise_mode_sde = options.get('noise_mode_sde', noise_mode_sde)
eta = options.get('eta', eta)
s_noise = options.get('s_noise', s_noise)
d_noise = options.get('d_noise', d_noise)
alpha_init = options.get('alpha_init', alpha_init)
k_init = options.get('k_init', k_init)
alpha_sde = options.get('alpha_sde', alpha_sde)
k_sde = options.get('k_sde', k_sde)
noise_seed_sde = options.get('noise_seed_sde', noise_seed+1)
c1 = options.get('c1', c1)
c2 = options.get('c2', c2)
c3 = options.get('c3', c3)
t_fn_formula = options.get('t_fn_formula', t_fn_formula)
sigma_fn_formula = options.get('sigma_fn_formula', sigma_fn_formula)
#unsampler_type = options.get('unsampler_type', unsampler_type)
if guides is not None:
guide_mode, rescale_floor, latent_guide_weight, latent_guide_weights, t_is, latent_guide, latent_guide_inv, latent_guide_mask, scheduler_, steps_, denoise_ = guides
"""if scheduler == "constant":
latent_guide_weights = initialize_or_scale(latent_guide_weights, latent_guide_weight, max_steps).to(default_dtype)
latent_guide_weights = F.pad(latent_guide_weights, (0, max_steps), value=0.0)"""
if scheduler_ != "constant":
latent_guide_weights = get_sigmas(model, scheduler_, steps_, denoise_).to(default_dtype)
latent_guide_weights = initialize_or_scale(latent_guide_weights, latent_guide_weight, max_steps).to(default_dtype)
latent_guide_weights = F.pad(latent_guide_weights, (0, max_steps), value=0.0)
if shift >= 0:
if isinstance(model.model.model_config, comfy.supported_models.SD3):
model = ModelSamplingSD3().patch(model, shift)[0]
elif isinstance(model.model.model_config, comfy.supported_models.AuraFlow):
model = ModelSamplingAuraFlow().patch_aura(model, shift)[0]
elif isinstance(model.model.model_config, comfy.supported_models.Stable_Cascade_C):
model = ModelSamplingStableCascade().patch(model, shift)[0]
if shift >= 0 and base_shift >= 0:
if isinstance(model.model.model_config, comfy.supported_models.Flux) or isinstance(model.model.model_config, comfy.supported_models.FluxSchnell):
model = ModelSamplingFlux().patch(model, shift, base_shift, latent_image['samples'].shape[3], latent_image['samples'].shape[2])[0]
latent = latent_image
latent_image_dtype = latent_image['samples'].dtype
if positive is None:
positive = [[
torch.zeros((1, 154, 4096)),
{'pooled_output': torch.zeros((1, 2048))}
]]
if negative is None:
negative = [[
torch.zeros((1, 154, 4096)),
{'pooled_output': torch.zeros((1, 2048))}
]]
if denoise_alt < 0:
d_noise = denoise_alt = -denoise_alt
if options is not None:
d_noise = options.get('d_noise', d_noise)
if sigmas is not None:
sigmas = sigmas.clone().to(default_dtype)
else:
sigmas = get_sigmas(model, scheduler, steps, denoise).to(default_dtype)
sigmas *= denoise_alt
if sampler_mode.startswith("unsample"):
null = torch.tensor([0.0], device=sigmas.device, dtype=sigmas.dtype)
sigmas = torch.flip(sigmas, dims=[0])
sigmas = torch.cat([sigmas, null])
elif sampler_mode.startswith("resample"):
null = torch.tensor([0.0], device=sigmas.device, dtype=sigmas.dtype)
sigmas = torch.cat([null, sigmas])
sigmas = torch.cat([sigmas, null])
if sampler_mode.startswith("unsample_"):
unsampler_type = sampler_mode.split("_", 1)[1]
elif sampler_mode.startswith("resample_"):
unsampler_type = sampler_mode.split("_", 1)[1]
else:
unsampler_type = ""
x = latent_image["samples"].clone().to(default_dtype)
if latent_image is not None:
if "samples_fp64" in latent_image:
if latent_image['samples'].shape == latent_image['samples_fp64'].shape:
if torch.norm(latent_image['samples'] - latent_image['samples_fp64']) < 0.01:
x = latent_image["samples_fp64"].clone()
if latent_noise is not None:
latent_noise["samples"] = latent_noise["samples"].clone().to(default_dtype)
if latent_noise_match is not None:
latent_noise_match["samples"] = latent_noise_match["samples"].clone().to(default_dtype)
if truncate_conditioning == "true" or truncate_conditioning == "true_and_zero_neg":
if positive is not None:
positive[0][0] = positive[0][0].clone().to(default_dtype)
positive[0][1]["pooled_output"] = positive[0][1]["pooled_output"].clone().to(default_dtype)
if negative is not None:
negative[0][0] = negative[0][0].clone().to(default_dtype)
negative[0][1]["pooled_output"] = negative[0][1]["pooled_output"].clone().to(default_dtype)
c = []
for t in positive:
d = t[1].copy()
pooled_output = d.get("pooled_output", None)
if pooled_output is not None:
d["pooled_output"] = d["pooled_output"][:, :2048]
n = [t[0][:, :154, :4096], d]
c.append(n)
positive = c
c = []
for t in negative:
d = t[1].copy()
pooled_output = d.get("pooled_output", None)
if pooled_output is not None:
if truncate_conditioning == "true_and_zero_neg":
d["pooled_output"] = torch.zeros((1,2048), dtype=t[0].dtype, device=t[0].device)
n = [torch.zeros((1,154,4096), dtype=t[0].dtype, device=t[0].device), d]
else:
d["pooled_output"] = d["pooled_output"][:, :2048]
n = [t[0][:, :154, :4096], d]
c.append(n)
negative = c
sigmin = model.model.model_sampling.sigma_min
sigmax = model.model.model_sampling.sigma_max
if noise_type_init == "none":
noise = torch.zeros_like(x)
elif latent_noise is None:
noise_sampler_init = NOISE_GENERATOR_CLASSES_SIMPLE.get(noise_type_init)(x=x, seed=seed, sigma_min=sigmin, sigma_max=sigmax)
if noise_type_init == "fractal":
noise_sampler_init.alpha = alpha_init
noise_sampler_init.k = k_init
noise_sampler_init.scale = 0.1
noise = noise_sampler_init(sigma=sigmax, sigma_next=sigmin)
else:
noise = latent_noise["samples"]
if noise_is_latent: #add noise and latent together and normalize --> noise
noise += x.cpu()
noise.sub_(noise.mean()).div_(noise.std())
if noise_normalize and noise.std() > 0:
noise.sub_(noise.mean()).div_(noise.std())
noise *= noise_stdev
noise = (noise - noise.mean()) + noise_mean
if latent_noise_match:
for i in range(latent_noise_match["samples"].shape[1]):
noise[0][i] = (noise[0][i] - noise[0][i].mean())
noise[0][i] = (noise[0][i]) + latent_noise_match["samples"][0][i].mean()
noise_mask = latent["noise_mask"] if "noise_mask" in latent else None
x0_output = {}
callback = latent_preview.prepare_callback(model, sigmas.shape[-1] - 1, x0_output)
disable_pbar = False
if noise_type_sde == "none":
eta_var = eta = 0.0
noise_type_sde = "gaussian"
if noise_mode_sde == "hard_var":
eta_var = eta
eta = 0.0
if cfg < 0:
cfgpp = -cfg
cfg = 1.0
sampler = comfy.samplers.ksampler("legacy_rk", {"eta": eta, "eta_var": eta_var, "s_noise": s_noise, "d_noise": d_noise, "alpha": alpha_sde, "k": k_sde, "c1": c1, "c2": c2, "c3": c3, "cfgpp": cfgpp, "MULTISTEP": multistep,
"noise_sampler_type": noise_type_sde, "noise_mode": noise_mode_sde, "noise_seed": noise_seed_sde, "rk_type": sampler_name, "implicit_sampler_name": implicit_sampler_name,
"exp_mode": exp_mode, "t_fn_formula": t_fn_formula, "sigma_fn_formula": sigma_fn_formula, "implicit_steps": implicit_steps,
"latent_guide": latent_guide, "latent_guide_inv": latent_guide_inv, "mask": latent_guide_mask,
"latent_guide_weights": latent_guide_weights, "guide_mode": guide_mode, #"unsampler_type": unsampler_type,
"LGW_MASK_RESCALE_MIN": rescale_floor, "sigmas_override": sigmas_override})
samples = comfy.sample.sample_custom(model, noise, cfg, sampler, sigmas, positive, negative, x.clone(),
noise_mask=noise_mask, callback=callback, disable_pbar=disable_pbar, seed=noise_seed)
out = latent.copy()
out["samples"] = samples
if "x0" in x0_output:
out_denoised = latent.copy()
out_denoised["samples"] = model.model.process_latent_out(x0_output["x0"].cpu())
else:
out_denoised = out
out["samples_fp64"] = out["samples"].clone()
out["samples"] = out["samples"].to(latent_image_dtype)
out_denoised["samples_fp64"] = out_denoised["samples"].clone()
out_denoised["samples"] = out_denoised["samples"].to(latent_image_dtype)
return ( out, out_denoised, )
class Legacy_SamplerRK:
@classmethod
def INPUT_TYPES(s):
return {"required":
{#"momentum": ("FLOAT", {"default": 0.0, "min": -100.0, "max": 100.0, "step":0.01, "round": False}),
"eta": ("FLOAT", {"default": 0.25, "min": -100.0, "max": 100.0, "step":0.01, "round": False, "tooltip": "Calculated noise amount to be added, then removed, after each step."}),
"eta_var": ("FLOAT", {"default": 0.0, "min": -100.0, "max": 100.0, "step":0.01, "round": False, "tooltip": "Calculate variance-corrected noise amount (overrides eta/noise_mode settings). Cannot be used at very low sigma values; reverts to eta/noise_mode for final steps."}),
"s_noise": ("FLOAT", {"default": 1.0, "min": -100.0, "max": 100.0, "step":0.01, "round": False, "tooltip": "Ratio of calculated noise amount actually added after each step. >1.0 will leave extra noise behind, <1.0 will remove more noise than it adds."}),
"d_noise": ("FLOAT", {"default": 1.0, "min": -100.0, "max": 100.0, "step":0.01, "round": False, "tooltip": "Ratio of calculated noise amount actually added after each step. >1.0 will leave extra noise behind, <1.0 will remove more noise than it adds."}),
"noise_mode": (["hard", "hard_sq", "soft", "softer", "exp"], {"default": 'hard', "tooltip": "How noise scales with the sigma schedule. Hard is the most aggressive, the others start strong and drop rapidly."}),
"noise_sampler_type": (NOISE_GENERATOR_NAMES, {"default": "brownian"}),
"alpha": ("FLOAT", {"default": 0.0, "min": -10000.0, "max": 10000.0, "step":0.1, "round": False, "tooltip": "Fractal noise mode: <0 = extra high frequency noise, >0 = extra low frequency noise, 0 = white noise."}),
"k": ("FLOAT", {"default": 1.0, "min": -10000.0, "max": 10000.0, "step":2.0, "round": False, "tooltip": "Fractal noise mode: all that matters is positive vs. negative. Effect unclear."}),
"noise_seed": ("INT", {"default": -1, "min": -1, "max": 0xffffffffffffffff, "tooltip": "Seed for the SDE noise that is added after each step if eta or eta_var are non-zero. If set to -1, it will use the increment the seed most recently used by the workflow."}),
"rk_type": (RK_SAMPLER_NAMES, {"default": "res_2m"}),
"exp_mode": ("BOOLEAN", {"default": False, "tooltip": "Convert linear RK methods to exponential form."}),
"multistep": ("BOOLEAN", {"default": False, "tooltip": "For samplers ending in S only. Reduces cost by one model call per step by reusing the previous step as the current predictor step."}),
"implicit_steps": ("INT", {"default": 0, "min": 0, "max": 100, "step":1, "tooltip": "Number of implicit Runge-Kutta refinement steps to run after each explicit step."}),
"cfgpp": ("FLOAT", {"default": 0.0, "min": -10000.0, "max": 10000.0, "step":0.01, "round": False, "tooltip": "CFG++ scale. Use in place of, or with, CFG. Currently only working with RES, DPMPP, and DDIM samplers."}),
"latent_guide_weight": ("FLOAT", {"default": 0.0, "min": -100.0, "max": 100.0, "step":0.01, "round": False}),
#"guide_mode": (["hard_light", "mean_std", "mean", "std", "noise_mean", "blend", "inversion"], {"default": 'mean', "tooltip": "The mode used. noise_mean and inversion are currently for test purposes only."}),
"guide_mode": (["hard_light", "mean_std", "mean", "std", "blend",], {"default": 'mean', "tooltip": "The mode used. noise_mean and inversion are currently for test purposes only."}),
#"guide_mode": (["hard_light", "blend", "mean_std", "mean", "std"], {"default": 'mean', "tooltip": "The mode used."}),
"rescale_floor": ("BOOLEAN", {"default": True, "tooltip": "Latent_guide_weight(s) control the minimum value for the latent_guide_mask. If false, they control the maximum value."}),
},
"optional":
{
"latent_guide": ("LATENT", ),
"latent_guide_inv": ("LATENT", ),
"latent_guide_mask": ("MASK", ),
"latent_guide_weights": ("SIGMAS", ),
"sigmas_override": ("SIGMAS", ),
}
}
RETURN_TYPES = ("SAMPLER",)
CATEGORY = "RES4LYF/legacy/samplers"
FUNCTION = "get_sampler"
def get_sampler(self, eta=0.25, eta_var=0.0, d_noise=1.0, s_noise=1.0, alpha=-1.0, k=1.0, cfgpp=0.0, multistep=False, noise_sampler_type="brownian", noise_mode="hard", noise_seed=-1, rk_type="dormand-prince",
exp_mode=False, t_fn_formula=None, sigma_fn_formula=None, implicit_steps=0,
latent_guide=None, latent_guide_inv=None, latent_guide_weight=0.0, guide_mode="hard_light", latent_guide_weights=None, latent_guide_mask=None, rescale_floor=True, sigmas_override=None,
):
sampler_name = "legacy_rk"
if latent_guide is None and latent_guide_inv is None:
latent_guide_weight = 0.0
steps = 10000
latent_guide_weights = initialize_or_scale(latent_guide_weights, latent_guide_weight, steps)
latent_guide_weights = F.pad(latent_guide_weights, (0, 10000), value=0.0)
sampler = comfy.samplers.ksampler(sampler_name, {"eta": eta, "eta_var": eta_var, "s_noise": s_noise, "d_noise": d_noise, "alpha": alpha, "k": k, "cfgpp": cfgpp, "MULTISTEP": multistep, "noise_sampler_type": noise_sampler_type, "noise_mode": noise_mode, "noise_seed": noise_seed, "rk_type": rk_type,
"exp_mode": exp_mode, "t_fn_formula": t_fn_formula, "sigma_fn_formula": sigma_fn_formula, "implicit_steps": implicit_steps,
"latent_guide": latent_guide, "latent_guide_inv": latent_guide_inv, "mask": latent_guide_mask, "latent_guide_weight": latent_guide_weight, "latent_guide_weights": latent_guide_weights, "guide_mode": guide_mode,
"LGW_MASK_RESCALE_MIN": rescale_floor, "sigmas_override": sigmas_override})
return (sampler, )
class Legacy_ClownsharKSamplerGuides:
@classmethod
def INPUT_TYPES(s):
return {"required":
{"guide_mode": (["hard_light", "mean_std", "mean", "std", "blend"], {"default": 'blend', "tooltip": "The mode used."}),
"latent_guide_weight": ("FLOAT", {"default": 0.0, "min": -100.0, "max": 100.0, "step":0.01, "round": False}),
"scheduler": (["constant"] + get_res4lyf_scheduler_list(), {"default": "beta57"},),
"steps": ("INT", {"default": 30, "min": 1, "max": 10000}),
"denoise": ("FLOAT", {"default": 1.0, "min": -100.0, "max": 100.0, "step":0.01, "round": False}),
"rescale_floor": ("BOOLEAN", {"default": False, "tooltip": "If true, latent_guide_weight(s) primarily affect the masked areas. If false, they control the unmasked areas."}),
},
"optional":
{
"latent_guide": ("LATENT", ),
"latent_guide_inv": ("LATENT", ),
"latent_guide_mask": ("MASK", ),
"latent_guide_weights": ("SIGMAS", ),
}
}
RETURN_TYPES = ("GUIDES",)
CATEGORY = "RES4LYF/legacy/samplers"
FUNCTION = "get_sampler"
def get_sampler(self, model=None, scheduler="constant", steps=30, denoise=1.0, latent_guide=None, latent_guide_inv=None, latent_guide_weight=0.0, guide_mode="blend", latent_guide_weights=None, latent_guide_mask=None, rescale_floor=True, t_is=None,
):
default_dtype = torch.float64
max_steps = 10000
#if scheduler != "constant":
# latent_guide_weights = get_sigmas(model, scheduler, steps, latent_guide_weight).to(default_dtype)
if scheduler == "constant":
latent_guide_weights = initialize_or_scale(None, latent_guide_weight, steps).to(default_dtype)
latent_guide_weights = F.pad(latent_guide_weights, (0, max_steps), value=0.0)
if latent_guide is not None:
x = latent_guide["samples"].clone().to(default_dtype)
if latent_guide_inv is not None:
x = latent_guide_inv["samples"].clone().to(default_dtype)
guides = (guide_mode, rescale_floor, latent_guide_weight, latent_guide_weights, t_is, latent_guide, latent_guide_inv, latent_guide_mask, scheduler, steps, denoise)
return (guides, )
class Legacy_SharkSampler:
@classmethod
def INPUT_TYPES(s):
return {"required":
{"model": ("MODEL",),
"add_noise": ("BOOLEAN", {"default": True}),
"noise_normalize": ("BOOLEAN", {"default": True}),
"noise_stdev": ("FLOAT", {"default": 1.0, "min": -10000.0, "max": 10000.0, "step":0.01, "round": False, }),
"noise_mean": ("FLOAT", {"default": 0.0, "min": -10000.0, "max": 10000.0, "step":0.01, "round": False, }),
"noise_is_latent": ("BOOLEAN", {"default": False}),
"noise_type": (NOISE_GENERATOR_NAMES, {"default": "gaussian"}),
"alpha": ("FLOAT", {"default": 1.0, "min": -10000.0, "max": 10000.0, "step":0.1, "round": False, }),
"k": ("FLOAT", {"default": 1.0, "min": -10000.0, "max": 10000.0, "step":2.0, "round": False, }),
"noise_seed": ("INT", {"default": 0, "min": -1, "max": 0xffffffffffffffff}),
"sampler_mode": (['standard', 'unsample', 'resample'],),
#"scheduler": (comfy.samplers.SCHEDULER_NAMES, ),
"scheduler": (get_res4lyf_scheduler_list(),),
"steps": ("INT", {"default": 30, "min": 1, "max": 10000}),
"denoise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10000, "step":0.01}),
"cfg": ("FLOAT", {"default": 5.0, "min": 0.0, "max": 100.0, "step":0.5, "round": False, }),
"truncate_conditioning": (['false', 'true', 'true_and_zero_neg'], ),
"positive": ("CONDITIONING", ),
"negative": ("CONDITIONING", ),
"sampler": ("SAMPLER", ),
"latent_image": ("LATENT", ),
},
"optional":
{
"sigmas": ("SIGMAS", ),
"latent_noise": ("LATENT", ),
"latent_noise_match": ("LATENT",),
}
}
RETURN_TYPES = ("LATENT","LATENT","LATENT","LATENT")
RETURN_NAMES = ("output", "denoised", "output_fp64", "denoised_fp64")
FUNCTION = "main"
CATEGORY = "RES4LYF/legacy/samplers"
def main(self, model, add_noise, noise_stdev, noise_mean, noise_normalize, noise_is_latent, noise_type, noise_seed, cfg, truncate_conditioning, alpha, k, positive, negative, sampler,
latent_image, sampler_mode, scheduler, steps, denoise, sigmas=None, latent_noise=None, latent_noise_match=None,):
latent = latent_image
latent_image_dtype = latent_image['samples'].dtype
default_dtype = torch.float64
if positive is None:
positive = [[
torch.zeros((1, 154, 4096)), # blah[0][0], a tensor of shape (1, 154, 4096)
{'pooled_output': torch.zeros((1, 2048))}
]]
if negative is None:
negative = [[
torch.zeros((1, 154, 4096)), # blah[0][0], a tensor of shape (1, 154, 4096)
{'pooled_output': torch.zeros((1, 2048))}
]]
if denoise < 0:
sampler.extra_options['d_noise'] = -denoise
denoise = 1.0
if sigmas is not None:
sigmas = sigmas.clone().to(default_dtype)
else:
sigmas = get_sigmas(model, scheduler, steps, denoise).to(default_dtype)
#sigmas = sigmas.clone().to(torch.float64)
if sampler_mode == "unsample":
null = torch.tensor([0.0], device=sigmas.device, dtype=sigmas.dtype)
sigmas = torch.flip(sigmas, dims=[0])
sigmas = torch.cat([sigmas, null])
elif sampler_mode == "resample":
null = torch.tensor([0.0], device=sigmas.device, dtype=sigmas.dtype)
sigmas = torch.cat([null, sigmas])
sigmas = torch.cat([sigmas, null])
if latent_image is not None:
x = latent_image["samples"].clone().to(default_dtype)
#x = {"samples": x}
if latent_noise is not None:
latent_noise["samples"] = latent_noise["samples"].clone().to(default_dtype)
if latent_noise_match is not None:
latent_noise_match["samples"] = latent_noise_match["samples"].clone().to(default_dtype)
if truncate_conditioning == "true" or truncate_conditioning == "true_and_zero_neg":
if positive is not None:
positive[0][0] = positive[0][0].clone().to(default_dtype)
positive[0][1]["pooled_output"] = positive[0][1]["pooled_output"].clone().to(default_dtype)
c = []
for t in positive:
d = t[1].copy()
pooled_output = d.get("pooled_output", None)
if pooled_output is not None:
d["pooled_output"] = d["pooled_output"][:, :2048]
n = [t[0][:, :154, :4096], d]
c.append(n)
positive = c
c = []
for t in negative:
if negative is not None:
negative[0][0] = negative[0][0].clone().to(default_dtype)
negative[0][1]["pooled_output"] = negative[0][1]["pooled_output"].clone().to(default_dtype)
d = t[1].copy()
pooled_output = d.get("pooled_output", None)
if pooled_output is not None:
if truncate_conditioning == "true_and_zero_neg":
d["pooled_output"] = torch.zeros((1,2048), dtype=t[0].dtype, device=t[0].device)
n = [torch.zeros((1,154,4096), dtype=t[0].dtype, device=t[0].device), d]
else:
d["pooled_output"] = d["pooled_output"][:, :2048]
n = [t[0][:, :154, :4096], d]
c.append(n)
negative = c
sigmin = model.model.model_sampling.sigma_min
sigmax = model.model.model_sampling.sigma_max
if noise_seed == -1:
seed = torch.initial_seed() + 1
else:
seed = noise_seed
torch.manual_seed(noise_seed)
noise_sampler = NOISE_GENERATOR_CLASSES.get(noise_type)(x=x, seed=seed, sigma_min=sigmin, sigma_max=sigmax)
if noise_type == "fractal":
noise_sampler.alpha = alpha
noise_sampler.k = k
noise_sampler.scale = 0.1
if not add_noise:
noise = torch.zeros_like(x)
elif latent_noise is None:
noise = noise_sampler(sigma=sigmax, sigma_next=sigmin)
else:
noise = latent_noise["samples"]
if noise_is_latent: #add noise and latent together and normalize --> noise
noise += x.cpu()
noise.sub_(noise.mean()).div_(noise.std())
if noise_normalize:
noise.sub_(noise.mean()).div_(noise.std())
noise *= noise_stdev
noise = (noise - noise.mean()) + noise_mean
if latent_noise_match:
for i in range(latent_noise_match["samples"].shape[1]):
noise[0][i] = (noise[0][i] - noise[0][i].mean())
noise[0][i] = (noise[0][i]) + latent_noise_match["samples"][0][i].mean()
noise_mask = latent["noise_mask"] if "noise_mask" in latent else None
x0_output = {}
callback = latent_preview.prepare_callback(model, sigmas.shape[-1] - 1, x0_output)
disable_pbar = False
samples = comfy.sample.sample_custom(model, noise, cfg, sampler, sigmas, positive, negative, x,
noise_mask=noise_mask, callback=callback, disable_pbar=disable_pbar, seed=noise_seed)
out = latent.copy()
out["samples"] = samples
if "x0" in x0_output:
out_denoised = latent.copy()
out_denoised["samples"] = model.model.process_latent_out(x0_output["x0"].cpu())
else:
out_denoised = out
out_orig_dtype = out['samples'].clone().to(latent_image_dtype)
out_denoised_orig_dtype = out_denoised['samples'].clone().to(latent_image_dtype)
return ( {'samples': out_orig_dtype}, {'samples': out_denoised_orig_dtype}, out, out_denoised,)