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samplers_extensions.py
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from .noise_classes import *
from .sigmas import get_sigmas
from .rk_sampler import sample_rk
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 torch
import torch.nn.functional as F
import math
import copy
from .helper import initialize_or_scale, get_res4lyf_scheduler_list, get_extra_options_kv, extra_options_flag
def move_to_same_device(*tensors):
if not tensors:
return tensors
device = tensors[0].device
return tuple(tensor.to(device) for tensor in tensors)
from .rk_coefficients_beta import RK_SAMPLER_NAMES_BETA_FOLDERS
from .noise_sigmas_timesteps_scaling import NOISE_MODE_NAMES
from .config import IMPLICIT_TYPE_NAMES
class ClownSamplerSelector_Beta:
@classmethod
def INPUT_TYPES(s):
return {"required":
{"sampler_name": (RK_SAMPLER_NAMES_BETA_FOLDERS, {"default": "multistep/res_2m"}),
},
"optional":
{
}
}
RETURN_TYPES = (RK_SAMPLER_NAMES_BETA_FOLDERS,)
RETURN_NAMES = ("sampler_name",)
FUNCTION = "main"
CATEGORY = "RES4LYF/sampler_options"
def main(self,sampler_name="res_2m",):
return (sampler_name,)
class ClownOptions_SDE_Beta:
@classmethod
def INPUT_TYPES(s):
return {"required":
{
"noise_type_sde": (NOISE_GENERATOR_NAMES_SIMPLE, {"default": "gaussian"}),
"noise_type_sde_substep": (NOISE_GENERATOR_NAMES_SIMPLE, {"default": "gaussian"}),
"noise_mode_sde": (NOISE_MODE_NAMES, {"default": 'hard', "tooltip": "How noise scales with the sigma schedule. Hard is the most aggressive, the others start strong and drop rapidly."}),
"noise_mode_sde_substep": (NOISE_MODE_NAMES, {"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": 0.5, "min": -100.0, "max": 100.0, "step":0.01, "round": False, "tooltip": "Calculated noise amount to be added, then removed, after each step."}),
"eta_substep": ("FLOAT", {"default": 0.5, "min": -100.0, "max": 100.0, "step":0.01, "round": False, "tooltip": "Calculated noise amount to be added, then removed, after each step."}),
},
"optional":
{
"options": ("OPTIONS", ),
}
}
RETURN_TYPES = ("OPTIONS",)
RETURN_NAMES = ("options",)
FUNCTION = "main"
CATEGORY = "RES4LYF/sampler_options"
def main(self, noise_type_sde="gaussian", noise_type_sde_substep="gaussian", noise_mode_sde="hard", noise_mode_sde_substep="hard", eta=0.5, eta_substep=0.5, options=None):
if options is None:
options = {}
options['noise_type_sde'] = noise_type_sde
options['noise_type_sde_substep'] = noise_type_sde_substep
options['noise_mode_sde'] = noise_mode_sde
options['noise_mode_sde_substep'] = noise_mode_sde_substep
options['eta'] = eta
options['eta_substep'] = eta_substep
return (options,)
class ClownOptions_StepSize_Beta:
@classmethod
def INPUT_TYPES(s):
return {"required":
{
"overshoot_mode": (NOISE_MODE_NAMES, {"default": 'hard', "tooltip": "How step size overshoot scales with the sigma schedule. Hard is the most aggressive, the others start strong and drop rapidly."}),
"overshoot_mode_substep": (NOISE_MODE_NAMES, {"default": 'hard', "tooltip": "How substep size overshoot scales with the sigma schedule. Hard is the most aggressive, the others start strong and drop rapidly."}),
"overshoot": ("FLOAT", {"default": 0.0, "min": -100.0, "max": 100.0, "step":0.01, "round": False, "tooltip": "Boost the size of each denoising step, then rescale to match the original. Has a softening effect."}),
"overshoot_substep": ("FLOAT", {"default": 0.0, "min": -100.0, "max": 100.0, "step":0.01, "round": False, "tooltip": "Boost the size of each denoising substep, then rescale to match the original. Has a softening effect."}),
},
"optional":
{
"options": ("OPTIONS", ),
}
}
RETURN_TYPES = ("OPTIONS",)
RETURN_NAMES = ("options",)
FUNCTION = "main"
CATEGORY = "RES4LYF/sampler_options"
def main(self, overshoot_mode="hard", overshoot_mode_substep="hard", overshoot=0.0, overshoot_substep=0.0, options=None):
if options is None:
options = {}
options['overshoot_mode'] = overshoot_mode
options['overshoot_mode_substep'] = overshoot_mode_substep
options['overshoot'] = overshoot
options['overshoot_substep'] = overshoot_substep
return (options,)
class ClownOptions_DetailBoost_Beta:
@classmethod
def INPUT_TYPES(s):
return {"required":
{
"noise_boost_step": ("FLOAT", {"default": 0.0, "min": -100.0, "max": 100.0, "step":0.01, "round": False, "tooltip": "Set to positive values to create a sharper, grittier, more detailed image. Set to negative values to soften and deepen the colors."}),
"noise_boost_substep": ("FLOAT", {"default": 0.0, "min": -100.0, "max": 100.0, "step":0.01, "round": False, "tooltip": "Set to positive values to create a sharper, grittier, more detailed image. Set to negative values to soften and deepen the colors."}),
"noise_anchor": ("FLOAT", {"default": 1.0, "min": -100.0, "max": 100.0, "step":0.01, "round": False, "tooltip": "Typically set to between 1.0 and 0.0. Lower values cerate a grittier, more detailed image."}),
"s_noise": ("FLOAT", {"default": 1.0, "min": -10000, "max": 10000, "step":0.01, "tooltip": "Adds extra SDE noise. Values around 1.03-1.07 can lead to a moderate boost in detail and paint textures."}),
"s_noise_substep": ("FLOAT", {"default": 1.0, "min": -10000, "max": 10000, "step":0.01, "tooltip": "Adds extra SDE noise. Values around 1.03-1.07 can lead to a moderate boost in detail and paint textures."}),
"d_noise": ("FLOAT", {"default": 1.0, "min": -10000, "max": 10000, "step":0.01, "tooltip": "Downscales the sigma schedule. Values around 0.98-0.95 can lead to a large boost in detail and paint textures."}),
},
"optional":
{
"options": ("OPTIONS", ),
}
}
RETURN_TYPES = ("OPTIONS",)
RETURN_NAMES = ("options",)
FUNCTION = "main"
CATEGORY = "RES4LYF/sampler_options"
def main(self, noise_boost_step=0.0, noise_boost_substep=0.0, noise_anchor=1.0, s_noise=1.0, s_noise_substep=1.0, d_noise=1.0, options=None):
if options is None:
options = {}
options['noise_boost_step'] = noise_boost_step
options['noise_boost_substep'] = noise_boost_substep
options['noise_anchor'] = noise_anchor
options['s_noise'] = s_noise
options['s_noise_substep'] = s_noise_substep
options['d_noise'] = d_noise
return (options,)
class ClownOptions_ImplicitSteps_Beta:
@classmethod
def INPUT_TYPES(s):
return {"required":
{
"implicit_type": (IMPLICIT_TYPE_NAMES, {"default": "predictor-corrector"}),
"implicit_type_substeps": (IMPLICIT_TYPE_NAMES, {"default": "predictor_corrector"}),
"implicit_steps": ("INT", {"default": 0, "min": 0, "max": 10000}),
"implicit_substeps": ("INT", {"default": 0, "min": 0, "max": 10000}),
},
"optional":
{
"options": ("OPTIONS", ),
}
}
RETURN_TYPES = ("OPTIONS",)
RETURN_NAMES = ("options",)
FUNCTION = "main"
CATEGORY = "RES4LYF/sampler_options"
def main(self, implicit_type="predictor-corrector", implicit_type_substeps="predictor-corrector", implicit_steps=0, implicit_substeps=0, options=None):
if options is None:
options = {}
options['implicit_type'] = implicit_type
options['implicit_type_substeps'] = implicit_type_substeps
options['implicit_steps'] = implicit_steps
options['implicit_substeps'] = implicit_substeps
return (options,)
class ClownOptions_ExtraOptions_Beta:
@classmethod
def INPUT_TYPES(s):
return {"required":
{
"extra_options": ("STRING", {"default": "", "multiline": True}),
},
"optional":
{
"options": ("OPTIONS", ),
}
}
RETURN_TYPES = ("OPTIONS",)
RETURN_NAMES = ("options",)
CATEGORY = "RES4LYF/sampler_options"
FUNCTION = "main"
def main(self, extra_options="",options=None):
if options is None:
options = {}
options['extra_options'] = extra_options
return (options, )
class ClownOptions_Automation_Beta:
@classmethod
def INPUT_TYPES(s):
return {"required":
{
},
"optional":
{
"etas": ("SIGMAS", ),
"etas_substep": ("SIGMAS", ),
"s_noises": ("SIGMAS", ),
"s_noises_substep": ("SIGMAS", ),
"epsilon_scales": ("SIGMAS", ),
"frame_weights": ("SIGMAS", ),
"options": ("OPTIONS", ),
}
}
RETURN_TYPES = ("OPTIONS",)
RETURN_NAMES = ("options",)
CATEGORY = "RES4LYF/sampler_options"
FUNCTION = "main"
def main(self, etas=None, etas_substep=None, s_noises=None, s_noises_substep=None, epsilon_scales=None, frame_weights=None, options=None):
if options is None:
options = {}
frame_weights_grp = (frame_weights, frame_weights)
automation = {
"etas": etas,
"etas_substep": etas_substep,
"s_noises": s_noises,
"s_noises_substep": s_noises_substep,
"epsilon_scales": epsilon_scales,
"frame_weights_grp": frame_weights_grp,
}
options["automation"] = automation
#options['automation'] = (etas, etas_substep, s_noises, s_noises_substep, epsilon_scales) # epsilon_scales was called unsample_resample_scales
return (options, )
class SharkOptions_Beta:
@classmethod
def INPUT_TYPES(s):
return {"required":
{
"noise_type_init": (NOISE_GENERATOR_NAMES_SIMPLE, {"default": "gaussian"}),
"noise_stdev": ("FLOAT", {"default": 1.0, "min": -10000.0, "max": 10000.0, "step":0.01, "round": False, }),
"sampler_mode": (['standard', 'unsample', 'resample'],),
"denoise_alt": ("FLOAT", {"default": 1.0, "min": -10000, "max": 10000, "step":0.01}),
"channelwise_cfg": ("BOOLEAN", {"default": False}),
},
"optional":
{
"sigmas": ("SIGMAS", ),
"options": ("OPTIONS", ),
}
}
RETURN_TYPES = ("OPTIONS",)
RETURN_NAMES = ("options",)
FUNCTION = "main"
CATEGORY = "RES4LYF/sampler_options"
def main(self, noise_type_init="gaussian", noise_stdev=1.0, sampler_mode="standard", denoise_alt=1.0, channelwise_cfg=False, options=None):
if options is None:
options = {}
options['noise_type_init'] = noise_type_init
options['noise_stdev'] = noise_stdev
options['sampler_mode'] = sampler_mode
options['denoise_alt'] = denoise_alt
options['channelwise_cfg'] = channelwise_cfg
return (options,)
class SamplerOptions_TimestepScaling:
# for patching the t_fn and sigma_fn (sigma <-> timestep) formulas to allow picking Runge-Kutta Ci values ("midpoints") with different scaling.
@classmethod
def INPUT_TYPES(s):
return {"required":
{
"sampler": ("SAMPLER", ),
"t_fn_formula": ("STRING", {"default": "1/((sigma).exp()+1)", "multiline": True}),
"sigma_fn_formula": ("STRING", {"default": "((1-t)/t).log()", "multiline": True}),
},
"optional":
{
}
}
RETURN_TYPES = ("SAMPLER",)
RETURN_NAMES = ("sampler",)
FUNCTION = "set_sampler_extra_options"
CATEGORY = "RES4LYF/sampler_extensions"
DESCRIPTION = "Patches ClownSampler's t_fn and sigma_fn (sigma <-> timestep) formulas to allow picking Runge-Kutta Ci values (midpoints) with different scaling."
def set_sampler_extra_options(self, sampler, t_fn_formula=None, sigma_fn_formula=None, ):
sampler = copy.deepcopy(sampler)
sampler.extra_options['t_fn_formula'] = t_fn_formula
sampler.extra_options['sigma_fn_formula'] = sigma_fn_formula
return (sampler, )
class SamplerOptions_GarbageCollection:
@classmethod
def INPUT_TYPES(s):
return {"required":
{
"sampler": ("SAMPLER", ),
"garbage_collection": ("BOOLEAN", {"default": True}),
},
"optional":
{
}
}
RETURN_TYPES = ("SAMPLER",)
RETURN_NAMES = ("sampler",)
FUNCTION = "set_sampler_extra_options"
CATEGORY = "RES4LYF/sampler_extensions"
DESCRIPTION = "Patches ClownSampler's to use garbage collection after every step. This can help with OOM issues during inference for large models like Flux. The tradeoff is slower sampling."
def set_sampler_extra_options(self, sampler, garbage_collection):
sampler = copy.deepcopy(sampler)
sampler.extra_options['GARBAGE_COLLECT'] = garbage_collection
return (sampler, )
GUIDE_MODE_NAMES = ["unsample",
"resample",
"epsilon",
"epsilon_projection",
"epsilon_dynamic_mean",
"epsilon_dynamic_mean_std",
"epsilon_dynamic_mean_from_bkg",
"epsilon_guide_mean_std_from_bkg",
"hard_light",
"blend",
"blend_projection",
"mean_std",
"mean",
"mean_tiled",
"std",
"data",
#"data_projection",
"none",
]
from .conditioning import FluxRegionalPrompt, FluxRegionalConditioning
from .models import ReFluxPatcher
class ClownGuidesFluxAdvanced_Beta: ##################################################################################################################################
@classmethod
def INPUT_TYPES(s):
return {"required":
{"regional_model": (["auto", "deactivate"], {"default": "auto"}),
"guide_mode": (GUIDE_MODE_NAMES_BETA_SIMPLE, {"default": 'epsilon', "tooltip": "Recommended: epsilon or mean/mean_std with sampler_mode = standard, and unsample/resample with sampler_mode = unsample/resample. Epsilon_dynamic_mean, etc. are only used with two latent inputs and a mask. Blend/hard_light/mean/mean_std etc. require low strengths, start with 0.01-0.02."}),
"channelwise_mode": ("BOOLEAN", {"default": False}),
"projection_mode": ("BOOLEAN", {"default": False}),
"guide_weight": ("FLOAT", {"default": 0.10, "min": -100.0, "max": 100.0, "step":0.01, "round": False, "tooltip": "Set the strength of the guide."}),
"guide_weight_bkg": ("FLOAT", {"default": 1.00, "min": -100.0, "max": 100.0, "step":0.01, "round": False, "tooltip": "Set the strength of the guide_bkg."}),
"guide_weight_scheduler": (["constant"] + get_res4lyf_scheduler_list(), {"default": "beta57"},),
"guide_weight_scheduler_bkg": (["constant"] + get_res4lyf_scheduler_list(), {"default": "constant"},),
"guide_end_step": ("INT", {"default": 15, "min": 1, "max": 10000}),
"guide_bkg_end_step": ("INT", {"default": 10000, "min": 1, "max": 10000}),
},
"optional":
{
"model": ("MODEL", ),
"positive_inpaint": ("CONDITIONING", ),
"positive_bkg": ("CONDITIONING", ),
"negative": ("CONDITIONING", ),
"guide": ("LATENT", ),
"guide_bkg": ("LATENT", ),
"mask": ("MASK", ),
"mask_bkg": ("MASK", ),
"guide_weights": ("SIGMAS", ),
"guide_weights_bkg": ("SIGMAS", ),
}
}
RETURN_TYPES = ("MODEL","CONDITIONING","CONDITIONING","LATENT","GUIDES",)
RETURN_NAMES = ("model","positive" ,"negative" ,"latent","guides",)
CATEGORY = "RES4LYF/sampler_extensions"
FUNCTION = "main"
def main(self, regional_model, guide_weight_scheduler="constant", guide_weight_scheduler_bkg="constant", guide_end_step=10000, guide_bkg_end_step=30, guide_weight_cutoff=1.0, guide_weight_bkg_cutoff=1.0, guide=None, guide_bkg=None, guide_weight=1.0, guide_weight_bkg=1.0,
guide_mode="epsilon", channelwise_mode=False, projection_mode=False, guide_weights=None, guide_weights_bkg=None, guide_mask_bkg=None,
model=None, positive_inpaint=None, positive_bkg=None, negative=None, latent_image=None, mask=None, mask_bkg=None,
):
default_dtype = torch.float64
max_steps = 10000
denoise, denoise_bkg = guide_weight_cutoff, guide_weight_bkg_cutoff
if projection_mode:
guide_mode = guide_mode + "_projection"
if channelwise_mode:
guide_mode = guide_mode + "_cw"
if guide_mode == "unsample_cw":
guide_mode = "unsample"
if guide_mode == "resample_cw":
guide_mode = "resample"
if guide_weight_scheduler == "constant" and guide_weights == None:
guide_weights = initialize_or_scale(None, 1.0, guide_end_step).to(default_dtype)
guide_weights = F.pad(guide_weights, (0, max_steps), value=0.0)
if guide_weight_scheduler_bkg == "constant" and guide_weights_bkg == None:
guide_weights_bkg = initialize_or_scale(None, 1.0, guide_bkg_end_step).to(default_dtype)
guide_weights_bkg = F.pad(guide_weights_bkg, (0, max_steps), value=0.0)
h, w = guide["samples"].shape[-2], guide["samples"].shape[-1]
if mask is None:
mask = torch.ones((1, 8*h, 8*w), dtype=guide["samples"].dtype, device=guide["samples"].device)
guide_mask = 1-mask if mask is not None else None
guide_mask_bkg = 1-mask_bkg if mask_bkg is not None else None
guide_mask_bkg = 1-guide_mask if guide_mask_bkg is None else guide_mask_bkg
guides = (guide_mode, guide_weight, guide_weight_bkg, guide_weights, guide_weights_bkg, guide, guide_bkg, guide_mask, guide_mask_bkg,
guide_weight_scheduler, guide_weight_scheduler_bkg, guide_end_step, guide_bkg_end_step, denoise, denoise_bkg)
latent = {'samples': torch.zeros_like(guide['samples'])}
if (positive_inpaint is None) and (positive_bkg is None):
positive = None
reflux_enable = False
elif mask is not None:
if regional_model == "auto":
reflux_enable = True
else:
reflux_enable = False
if positive_bkg is None:
if positive_bkg is None:
positive_bkg = [[
torch.zeros((1, 256, 4096)),
{'pooled_output': torch.zeros((1, 768))}
]]
cond_regional, mask_inv = FluxRegionalPrompt().main(cond=positive_inpaint, mask=mask)
cond_regional, mask_inv_inv = FluxRegionalPrompt().main(cond=positive_bkg , cond_regional=cond_regional, mask=mask_inv)
positive, = FluxRegionalConditioning().main(conditioning_regional=cond_regional, self_attn_floor=0.0)
else:
positive = positive_inpaint
reflux_enable = False
model, = ReFluxPatcher().main(model, enable=reflux_enable)
return (model, positive, negative, latent, guides, )
class ClownInpaint: ##################################################################################################################################
@classmethod
def INPUT_TYPES(s):
return {"required":
{#"guide_mode": (GUIDE_MODE_NAMES, {"default": 'epsilon', "tooltip": "Recommended: epsilon or mean/mean_std with sampler_mode = standard, and unsample/resample with sampler_mode = unsample/resample. Epsilon_dynamic_mean, etc. are only used with two latent inputs and a mask. Blend/hard_light/mean/mean_std etc. require low strengths, start with 0.01-0.02."}),
"guide_weight": ("FLOAT", {"default": 0.10, "min": -100.0, "max": 100.0, "step":0.01, "round": False, "tooltip": "Set the strength of the guide."}),
"guide_weight_bkg": ("FLOAT", {"default": 1.00, "min": -100.0, "max": 100.0, "step":0.01, "round": False, "tooltip": "Set the strength of the guide_bkg."}),
"guide_weight_scheduler": (["constant"] + get_res4lyf_scheduler_list(), {"default": "beta57"},),
"guide_weight_scheduler_bkg": (["constant"] + get_res4lyf_scheduler_list(), {"default": "constant"},),
"guide_end_step": ("INT", {"default": 15, "min": 1, "max": 10000}),
"guide_bkg_end_step": ("INT", {"default": 10000, "min": 1, "max": 10000}),
},
"optional":
{
"model": ("MODEL", ),
"positive_inpaint": ("CONDITIONING", ),
"positive_bkg": ("CONDITIONING", ),
"negative": ("CONDITIONING", ),
"latent_image": ("LATENT", ),
"mask": ("MASK", ),
"guide_weights": ("SIGMAS", ),
"guide_weights_bkg": ("SIGMAS", ),
}
}
RETURN_TYPES = ("MODEL","CONDITIONING","CONDITIONING","LATENT","GUIDES",)
RETURN_NAMES = ("model","positive" ,"negative" ,"latent","guides",)
CATEGORY = "RES4LYF/sampler_extensions"
FUNCTION = "main"
def main(self, guide_weight_scheduler="constant", guide_weight_scheduler_bkg="constant", guide_end_step=10000, guide_bkg_end_step=30, guide_weight_scale=1.0, guide_weight_bkg_scale=1.0, guide=None, guide_bkg=None, guide_weight=1.0, guide_weight_bkg=1.0,
guide_mode="epsilon", guide_weights=None, guide_weights_bkg=None, guide_mask_bkg=None,
model=None, positive_inpaint=None, positive_bkg=None, negative=None, latent_image=None, mask=None,
):
default_dtype = torch.float64
guide = latent_image
guide_bkg = {'samples': latent_image['samples'].clone()}
max_steps = 10000
denoise, denoise_bkg = guide_weight_scale, guide_weight_bkg_scale
if guide_mode.startswith("epsilon_") and not guide_mode.startswith("epsilon_projection") and guide_bkg == None:
print("Warning: need two latent inputs for guide_mode=",guide_mode," to work. Falling back to epsilon.")
guide_mode = "epsilon"
if guide_weight_scheduler == "constant":
guide_weights = initialize_or_scale(None, guide_weight, guide_end_step).to(default_dtype)
guide_weights = F.pad(guide_weights, (0, max_steps), value=0.0)
if guide_weight_scheduler_bkg == "constant":
guide_weights_bkg = initialize_or_scale(None, guide_weight_bkg, guide_bkg_end_step).to(default_dtype)
guide_weights_bkg = F.pad(guide_weights_bkg, (0, max_steps), value=0.0)
guides = (guide_mode, guide_weight, guide_weight_bkg, guide_weights, guide_weights_bkg, guide, guide_bkg, mask, guide_mask_bkg,
guide_weight_scheduler, guide_weight_scheduler_bkg, guide_end_step, guide_bkg_end_step, denoise, denoise_bkg)
latent = {'samples': torch.zeros_like(latent_image['samples'])}
if (positive_inpaint is None) and (positive_bkg is None):
positive = None
else:
if positive_bkg is None:
if positive_bkg is None:
positive_bkg = [[
torch.zeros((1, 256, 4096)),
{'pooled_output': torch.zeros((1, 768))}
]]
cond_regional, mask_inv = FluxRegionalPrompt().main(cond=positive_inpaint, mask=mask)
cond_regional, mask_inv_inv = FluxRegionalPrompt().main(cond=positive_bkg , cond_regional=cond_regional, mask=mask_inv)
positive, = FluxRegionalConditioning().main(conditioning_regional=cond_regional, self_attn_floor=0.0)
model, = ReFluxPatcher().main(model, enable=True)
return (model, positive, negative, latent, guides, )
class ClownInpaintSimple: ##################################################################################################################################
@classmethod
def INPUT_TYPES(s):
return {"required":
{#"guide_mode": (GUIDE_MODE_NAMES, {"default": 'epsilon', "tooltip": "Recommended: epsilon or mean/mean_std with sampler_mode = standard, and unsample/resample with sampler_mode = unsample/resample. Epsilon_dynamic_mean, etc. are only used with two latent inputs and a mask. Blend/hard_light/mean/mean_std etc. require low strengths, start with 0.01-0.02."}),
"guide_weight": ("FLOAT", {"default": 0.10, "min": -100.0, "max": 100.0, "step":0.01, "round": False, "tooltip": "Set the strength of the guide."}),
"guide_weight_scheduler": (["constant"] + get_res4lyf_scheduler_list(), {"default": "beta57"},),
"guide_end_step": ("INT", {"default": 15, "min": 1, "max": 10000}),
},
"optional":
{
"model": ("MODEL", ),
"positive_inpaint": ("CONDITIONING", ),
"negative": ("CONDITIONING", ),
"latent_image": ("LATENT", ),
"mask": ("MASK", ),
}
}
RETURN_TYPES = ("MODEL","CONDITIONING","CONDITIONING","LATENT","GUIDES",)
RETURN_NAMES = ("model","positive" ,"negative" ,"latent","guides",)
CATEGORY = "RES4LYF/sampler_extensions"
FUNCTION = "main"
def main(self, guide_weight_scheduler="constant", guide_weight_scheduler_bkg="constant", guide_end_step=10000, guide_bkg_end_step=30, guide_weight_scale=1.0, guide_weight_bkg_scale=1.0, guide=None, guide_bkg=None, guide_weight=1.0, guide_weight_bkg=1.0,
guide_mode="epsilon", guide_weights=None, guide_weights_bkg=None, guide_mask_bkg=None,
model=None, positive_inpaint=None, positive_bkg=None, negative=None, latent_image=None, mask=None,
):
default_dtype = torch.float64
guide = latent_image
guide_bkg = {'samples': latent_image['samples'].clone()}
max_steps = 10000
denoise, denoise_bkg = guide_weight_scale, guide_weight_bkg_scale
if guide_mode.startswith("epsilon_") and not guide_mode.startswith("epsilon_projection") and guide_bkg == None:
print("Warning: need two latent inputs for guide_mode=",guide_mode," to work. Falling back to epsilon.")
guide_mode = "epsilon"
if guide_weight_scheduler == "constant":
guide_weights = initialize_or_scale(None, guide_weight, guide_end_step).to(default_dtype)
guide_weights = F.pad(guide_weights, (0, max_steps), value=0.0)
if guide_weight_scheduler_bkg == "constant":
guide_weights_bkg = initialize_or_scale(None, guide_weight_bkg, guide_bkg_end_step).to(default_dtype)
guide_weights_bkg = F.pad(guide_weights_bkg, (0, max_steps), value=0.0)
guides = (guide_mode, guide_weight, guide_weight_bkg, guide_weights, guide_weights_bkg, guide, guide_bkg, mask, guide_mask_bkg,
guide_weight_scheduler, guide_weight_scheduler_bkg, guide_end_step, guide_bkg_end_step, denoise, denoise_bkg)
latent = {'samples': torch.zeros_like(latent_image['samples'])}
if (positive_inpaint is None) and (positive_bkg is None):
positive = None
else:
if positive_bkg is None:
if positive_bkg is None:
positive_bkg = [[
torch.zeros((1, 256, 4096)),
{'pooled_output': torch.zeros((1, 768))}
]]
cond_regional, mask_inv = FluxRegionalPrompt().main(cond=positive_inpaint, mask=mask)
cond_regional, mask_inv_inv = FluxRegionalPrompt().main(cond=positive_bkg , cond_regional=cond_regional, mask=mask_inv)
positive, = FluxRegionalConditioning().main(conditioning_regional=cond_regional, self_attn_floor=1.0)
model, = ReFluxPatcher().main(model, enable=True)
return (model, positive, negative, latent, guides, )
##################################################################################################################################
class ClownsharKSamplerGuide:
@classmethod
def INPUT_TYPES(s):
return {"required":
{"guide_mode": (GUIDE_MODE_NAMES, {"default": 'epsilon_projection', "tooltip": "Recommended: epsilon or mean/mean_std with sampler_mode = standard, and unsample/resample with sampler_mode = unsample/resample. Epsilon_dynamic_mean, etc. are only used with two latent inputs and a mask. Blend/hard_light/mean/mean_std etc. require low strengths, start with 0.01-0.02."}),
"guide_weight": ("FLOAT", {"default": 0.75, "min": -100.0, "max": 100.0, "step":0.01, "round": False, "tooltip": "Set the strength of the guide."}),
#"guide_weight_bkg": ("FLOAT", {"default": 0.75, "min": -100.0, "max": 100.0, "step":0.01, "round": False, "tooltip": "Set the strength of the guide_bkg."}),
"guide_weight_scale": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step":0.01, "round": False, "tooltip": "Disables the guide for the next step when the denoised image is similar to the guide. Higher values will strengthen the effect."}),
#"guide_weight_bkg_scale": ("FLOAT", {"default": 1.0, "min": -100.0, "max": 100.0, "step":0.01, "round": False, "tooltip": "Disables the guide for the next step when the denoised image is similar to the guide. Higher values will strengthen the effect."}),
"guide_weight_scheduler": (["constant"] + get_res4lyf_scheduler_list(), {"default": "beta57"},),
#"guide_weight_scheduler_bkg": (["constant"] + comfy.samplers.SCHEDULER_NAMES + ["beta57"], {"default": "beta57"},),
"guide_end_step": ("INT", {"default": 15, "min": 1, "max": 10000}),
#"guide_bkg_end_step": ("INT", {"default": 15, "min": 1, "max": 10000}),
},
"optional":
{
"guide": ("LATENT", ),
#"guide_bkg": ("LATENT", ),
"guide_mask": ("MASK", ),
#"guide_mask_bkg": ("MASK", ),
"guide_weights": ("SIGMAS", ),
#"guide_weights_bkg": ("SIGMAS", ),
}
}
RETURN_TYPES = ("GUIDES",)
RETURN_NAMES = ("guides",)
CATEGORY = "RES4LYF/sampler_extensions"
FUNCTION = "main"
def main(self, guide_weight_scheduler="constant", guide_weight_scheduler_bkg="constant", guide_end_step=30, guide_bkg_end_step=30, guide_weight_scale=1.0, guide_weight_bkg_scale=1.0, guide=None, guide_bkg=None, guide_weight=0.0, guide_weight_bkg=0.0,
guide_mode="blend", guide_weights=None, guide_weights_bkg=None, guide_mask=None, guide_mask_bkg=None,
):
default_dtype = torch.float64
max_steps = 10000
denoise, denoise_bkg = guide_weight_scale, guide_weight_bkg_scale
if guide_mode.startswith("epsilon_") and not guide_mode.startswith("epsilon_projection") and guide_bkg == None:
print("Warning: need two latent inputs for guide_mode=",guide_mode," to work. Falling back to epsilon.")
guide_mode = "epsilon"
if guide_weight_scheduler == "constant" and guide_weights == None:
guide_weights = initialize_or_scale(None, 1.0, guide_end_step).to(default_dtype)
#guide_weights = initialize_or_scale(None, guide_weight, guide_end_step).to(default_dtype)
guide_weights = F.pad(guide_weights, (0, max_steps), value=0.0)
if guide_weight_scheduler_bkg == "constant":
guide_weights_bkg = initialize_or_scale(None, 0.0, guide_bkg_end_step).to(default_dtype)
#guide_weights_bkg = initialize_or_scale(None, guide_weight_bkg, guide_bkg_end_step).to(default_dtype)
guide_weights_bkg = F.pad(guide_weights_bkg, (0, max_steps), value=0.0)
guides = (guide_mode, guide_weight, guide_weight_bkg, guide_weights, guide_weights_bkg, guide, guide_bkg, guide_mask, guide_mask_bkg,
guide_weight_scheduler, guide_weight_scheduler_bkg, guide_end_step, guide_bkg_end_step, denoise, denoise_bkg)
return (guides, )
class ClownsharKSamplerGuides:
@classmethod
def INPUT_TYPES(s):
return {"required":
{"guide_mode": (GUIDE_MODE_NAMES, {"default": 'epsilon_projection', "tooltip": "Recommended: epsilon or mean/mean_std with sampler_mode = standard, and unsample/resample with sampler_mode = unsample/resample. Epsilon_dynamic_mean, etc. are only used with two latent inputs and a mask. Blend/hard_light/mean/mean_std etc. require low strengths, start with 0.01-0.02."}),
"guide_weight": ("FLOAT", {"default": 0.75, "min": -100.0, "max": 100.0, "step":0.01, "round": False, "tooltip": "Set the strength of the guide."}),
"guide_weight_bkg": ("FLOAT", {"default": 0.75, "min": -100.0, "max": 100.0, "step":0.01, "round": False, "tooltip": "Set the strength of the guide_bkg."}),
"guide_weight_scale": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step":0.01, "round": False, "tooltip": "Disables the guide for the next step when the denoised image is similar to the guide. Higher values will strengthen the effect."}),
"guide_weight_bkg_scale": ("FLOAT", {"default": 1.0, "min": -100.0, "max": 100.0, "step":0.01, "round": False, "tooltip": "Disables the guide for the next step when the denoised image is similar to the guide. Higher values will strengthen the effect."}),
"guide_weight_scheduler": (["constant"] + get_res4lyf_scheduler_list(), {"default": "beta57"},),
"guide_weight_scheduler_bkg": (["constant"] + get_res4lyf_scheduler_list(), {"default": "constant"},),
"guide_end_step": ("INT", {"default": 15, "min": 1, "max": 10000}),
"guide_bkg_end_step": ("INT", {"default": 15, "min": 1, "max": 10000}),
},
"optional":
{
"guide": ("LATENT", ),
"guide_bkg": ("LATENT", ),
"guide_mask": ("MASK", ),
"guide_mask_bkg": ("MASK", ),
"guide_weights": ("SIGMAS", ),
"guide_weights_bkg": ("SIGMAS", ),
}
}
RETURN_TYPES = ("GUIDES",)
RETURN_NAMES = ("guides",)
CATEGORY = "RES4LYF/sampler_extensions"
FUNCTION = "main"
def main(self, guide_weight_scheduler="constant", guide_weight_scheduler_bkg="constant", guide_end_step=30, guide_bkg_end_step=30, guide_weight_scale=1.0, guide_weight_bkg_scale=1.0, guide=None, guide_bkg=None, guide_weight=0.0, guide_weight_bkg=0.0,
guide_mode="blend", guide_weights=None, guide_weights_bkg=None, guide_mask=None, guide_mask_bkg=None,
):
default_dtype = torch.float64
max_steps = 10000
denoise, denoise_bkg = guide_weight_scale, guide_weight_bkg_scale
if guide_mode.startswith("epsilon_") and not guide_mode.startswith("epsilon_projection") and guide_bkg == None:
print("Warning: need two latent inputs for guide_mode=",guide_mode," to work. Falling back to epsilon.")
guide_mode = "epsilon"
if guide_weight_scheduler == "constant" and guide_weights == None:
guide_weights = initialize_or_scale(None, 1.0, guide_end_step).to(default_dtype)
guide_weights = F.pad(guide_weights, (0, max_steps), value=0.0)
if guide_weight_scheduler_bkg == "constant" and guide_weights_bkg == None:
guide_weights_bkg = initialize_or_scale(None, 1.0, guide_bkg_end_step).to(default_dtype)
guide_weights_bkg = F.pad(guide_weights_bkg, (0, max_steps), value=0.0)
guides = (guide_mode, guide_weight, guide_weight_bkg, guide_weights, guide_weights_bkg, guide, guide_bkg, guide_mask, guide_mask_bkg,
guide_weight_scheduler, guide_weight_scheduler_bkg, guide_end_step, guide_bkg_end_step, denoise, denoise_bkg)
return (guides, )
GUIDE_MODE_NAMES_BETA_MASTER_LIST = ["unsample",
"resample",
"unsample_projection",
"resample_projection",
"unsample_projection_cw",
"resample_projection_cw",
"pseudoimplicit",
"pseudoimplicit_cw",
"pseudoimplicit_projection",
"pseudoimplicit_projection_cw",
"fully_pseudoimplicit",
"fully_pseudoimplicit_cw",
"fully_pseudoimplicit_projection",
"fully_pseudoimplicit_projection_cw",
"epsilon",
"epsilon_cw",
"epsilon_projection",
"epsilon_projection_cw",
"epsilon_dynamic_mean",
"epsilon_dynamic_mean_std",
"epsilon_dynamic_mean_from_bkg",
"epsilon_guide_mean_std_from_bkg",
"hard_light",
"blend",
"blend_projection",
"mean_std",
"mean",
"mean_tiled",
"std",
"data",
#"data_projection",
"none",
]
GUIDE_MODE_NAMES_BETA_MISC = [
"hard_light",
"blend",
"blend_projection",
"mean_std",
"mean",
"mean_tiled",
"std",
"data",
#"data_projection",
"none",
]
GUIDE_MODE_NAMES_BETA = ["unsample",
"resample",
"unsample_projection",
"resample_projection",
"epsilon",
"epsilon_projection",
"pseudoimplicit",
"pseudoimplicit_projection",
"fully_pseudoimplicit",
"fully_pseudoimplicit_projection",
"epsilon_dynamic_mean",
"epsilon_dynamic_mean_std",
"epsilon_dynamic_mean_from_bkg",
"epsilon_guide_mean_std_from_bkg",
"hard_light",
"blend",
"blend_projection",
"mean_std",
"mean",
"mean_tiled",
"std",
"data",
#"data_projection",
"none",
]
GUIDE_MODE_NAMES_BETA_SIMPLE = [
"epsilon",
"pseudoimplicit",
"fully_pseudoimplicit",
"none",
]
GUIDE_MODE_NAMES_BETA_CHANNELWISE_SUPPORTED = [
"unsample",
"resample_projection",
"pseudoimplicit",
"pseudoimplicit_projection",
"fully_pseudoimplicit",
"fully_pseudoimplicit_projection",
"epsilon",
"epsilon_projection",
]
class ClownsharKSamplerGuideMisc_Beta:
@classmethod
def INPUT_TYPES(s):
return {"required":
{"guide_mode": (GUIDE_MODE_NAMES_BETA_MISC, {"default": 'blend',
"tooltip": "Blend/hard_light/mean/mean_std etc. require low strengths, start with 0.01-0.02."}),
"guide_weight": ("FLOAT", {"default": 0.05, "min": -100.0, "max": 100.0, "step":0.01, "round": False,
"tooltip": "Set the strength of the guide."}),
"guide_weight_cutoff": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step":0.01, "round": False,
"tooltip": "Disables the guide for the next step when the denoised image is similar to the guide. Higher values will strengthen the effect."}),
"guide_weight_scheduler": (["constant"] + get_res4lyf_scheduler_list(), {"default": "beta57"},),
"guide_end_step": ("INT", {"default": 15, "min": 1, "max": 10000}),
},
"optional":
{
"guide": ("LATENT", ),
"guide_mask": ("MASK", ),
"guide_weights": ("SIGMAS", ),
}
}
RETURN_TYPES = ("GUIDES",)
RETURN_NAMES = ("guides",)
CATEGORY = "RES4LYF/sampler_extensions"
FUNCTION = "main"
def main(self, guide_weight_scheduler="constant", guide_weight_scheduler_bkg="constant", guide_end_step=30, guide_bkg_end_step=30, guide_weight_cutoff=1.0, guide_weight_bkg_cutoff=1.0,
guide=None, guide_bkg=None, guide_weight=0.0, guide_weight_bkg=0.0,
guide_mode="blend", guide_weights=None, guide_weights_bkg=None, guide_mask=None, guide_mask_bkg=None,
):
default_dtype = torch.float64
max_steps = 10000
denoise, denoise_bkg = guide_weight_cutoff, guide_weight_bkg_cutoff
if guide_mode.startswith("epsilon_") and not guide_mode.startswith("epsilon_projection") and guide_bkg == None:
print("Warning: need two latent inputs for guide_mode=",guide_mode," to work. Falling back to epsilon.")
guide_mode = "epsilon"
if guide_weight_scheduler == "constant" and guide_weights == None:
guide_weights = initialize_or_scale(None, 1.0, guide_end_step).to(default_dtype)
guide_weights = F.pad(guide_weights, (0, max_steps), value=0.0)
if guide_weight_scheduler_bkg == "constant":
guide_weights_bkg = initialize_or_scale(None, 0.0, guide_bkg_end_step).to(default_dtype)
guide_weights_bkg = F.pad(guide_weights_bkg, (0, max_steps), value=0.0)
guides = (guide_mode, guide_weight, guide_weight_bkg, guide_weights, guide_weights_bkg, guide, guide_bkg, guide_mask, guide_mask_bkg,
guide_weight_scheduler, guide_weight_scheduler_bkg, guide_end_step, guide_bkg_end_step, denoise, denoise_bkg)
return (guides, )
class ClownsharKSamplerGuidesMisc_Beta:
@classmethod
def INPUT_TYPES(s):
return {"required":
{"guide_mode": (GUIDE_MODE_NAMES_BETA_MISC, {"default": 'blend',
"tooltip": "Blend/hard_light/mean/mean_std etc. require low strengths, start with 0.01-0.02."}),
"guide_weight": ("FLOAT", {"default": 0.05, "min": -100.0, "max": 100.0, "step":0.01, "round": False,
"tooltip": "Set the strength of the guide."}),
"guide_weight_bkg": ("FLOAT", {"default": 0.75, "min": -100.0, "max": 100.0, "step":0.01, "round": False,
"tooltip": "Set the strength of the guide_bkg."}),
"guide_weight_cutoff": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step":0.01, "round": False,
"tooltip": "Disables the guide for the next step when the denoised image is similar to the guide. Higher values will strengthen the effect."}),
"guide_weight_bkg_cutoff": ("FLOAT", {"default": 1.0, "min": -100.0, "max": 100.0, "step":0.01, "round": False,
"tooltip": "Disables the guide for the next step when the denoised image is similar to the guide. Higher values will strengthen the effect."}),
"guide_weight_scheduler": (["constant"] + get_res4lyf_scheduler_list(), {"default": "beta57"},),
"guide_weight_scheduler_bkg": (["constant"] + get_res4lyf_scheduler_list(), {"default": "constant"},),
"guide_end_step": ("INT", {"default": 15, "min": 1, "max": 10000}),
"guide_bkg_end_step": ("INT", {"default": 15, "min": 1, "max": 10000}),
},
"optional":
{
"guide": ("LATENT", ),
"guide_bkg": ("LATENT", ),
"guide_mask": ("MASK", ),
"guide_mask_bkg": ("MASK", ),
"guide_weights": ("SIGMAS", ),
"guide_weights_bkg": ("SIGMAS", ),
}
}
RETURN_TYPES = ("GUIDES",)
RETURN_NAMES = ("guides",)
CATEGORY = "RES4LYF/sampler_extensions"
FUNCTION = "main"
def main(self, guide_weight_scheduler="constant", guide_weight_scheduler_bkg="constant", guide_end_step=30, guide_bkg_end_step=30, guide_weight_cutoff=1.0, guide_weight_bkg_cutoff=1.0,
guide=None, guide_bkg=None, guide_weight=0.0, guide_weight_bkg=0.0,
guide_mode="blend", guide_weights=None, guide_weights_bkg=None, guide_mask=None, guide_mask_bkg=None,
):
default_dtype = torch.float64
max_steps = 10000
denoise, denoise_bkg = guide_weight_cutoff, guide_weight_bkg_cutoff
if guide_mode.startswith("epsilon_") and not guide_mode.startswith("epsilon_projection") and guide_bkg == None: