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import random
import comfy
import comfy.model_management
import comfy.sample
import comfy.samplers
import comfy.utils
from comfy.utils import ProgressBar
import numpy as np
import torch
from ..utils import VyroParams
from ..utils.restart_sampling import (
DDIMWrapper,
KSamplerRestartWrapper,
RESWrapper,
SCHEDULER_MAPPING,
_restart_scheduler,
_restart_segments,
_total_steps,
prepare_restart_segments,
)
from ..utils.sdxl_ksampler import CfgMethods, sdxl_ksampler
def get_supported_samplers():
samplers = comfy.samplers.KSampler.SAMPLERS.copy()
samplers.remove("uni_pc")
samplers.remove("uni_pc_bh2")
# SDE samplers cannot be used with restarts
samplers.remove("dpmpp_sde")
samplers.remove("dpmpp_sde_gpu")
samplers.remove("dpmpp_2m_sde")
samplers.remove("dpmpp_2m_sde_gpu")
samplers.append("res")
return samplers
def get_supported_restart_schedulers():
return list(SCHEDULER_MAPPING.keys())
def sdxl_restarts_ksampler(base_model, refiner_model, seed, base_steps, refiner_steps, cfg, sampler_name, scheduler, base_positive, base_negative, refiner_positive, refiner_negative, latent, segments, restart_scheduler, denoise=1.0, disable_noise=False, start_step=None, last_step=None, force_full_denoise=False, refiner_detail_boost=0.0,cfg_clamp_after_step=0):
global _total_steps, _restart_segments, _restart_scheduler
_restart_scheduler = restart_scheduler
_restart_segments = prepare_restart_segments(segments)
if sampler_name == "res":
sampler_wrapper = RESWrapper(start_step, last_step, _restart_segments, _restart_scheduler, cfg_clamp_after_step)
elif sampler_name == "ddim":
sampler_wrapper = DDIMWrapper(start_step, last_step, _restart_segments, _restart_scheduler, cfg_clamp_after_step)
else:
sampler_wrapper = KSamplerRestartWrapper(sampler_name, start_step, last_step, _restart_segments, _restart_scheduler, cfg_clamp_after_step)
# Add the additional steps to the progress bar
pbar_update_absolute = ProgressBar.update_absolute
def pbar_update_absolute_wrapper(self, value, total=None, preview=None):
pbar_update_absolute(self, value, _total_steps, preview)
ProgressBar.update_absolute = pbar_update_absolute_wrapper
try:
result = sdxl_ksampler(base_model, refiner_model, seed, base_steps, refiner_steps, cfg, sampler_name,
scheduler, base_positive, base_negative, refiner_positive, refiner_negative,
latent, denoise=denoise, disable_noise=False,
start_step=0, last_step=last_step, force_full_denoise=force_full_denoise,
dynamic_base_cfg=cfg, dynamic_refiner_cfg=cfg,
cfg_method=CfgMethods.TONEMAP, refiner_detail_boost=refiner_detail_boost, restart_wrapper=sampler_wrapper)
return result
finally:
sampler_wrapper.cleanup()
ProgressBar.update_absolute = pbar_update_absolute
class VyroSDXLSampler:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"params": ("VYRO_PARAMS",),
"base_model": ("MODEL",),
"base_positive": ("CONDITIONING", ),
"base_negative": ("CONDITIONING", ),
"sampler_name": (get_supported_samplers(), ),
"scheduler": (comfy.samplers.KSampler.SCHEDULERS, ),
"base_ratio": ("FLOAT", {"default": 0.8, "min": 0.0, "max": 1.0, "step": 0.01}),
"denoise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}),
"refiner_detail_boost": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.05},),
},
"optional": {
"refiner_model": ("MODEL",),
"refiner_positive": ("CONDITIONING", ),
"refiner_negative": ("CONDITIONING", ),
"latent_image": ("LATENT",),
}
}
RETURN_TYPES = ("LATENT",)
FUNCTION = "sample"
CATEGORY = "Vyro/Samplers"
def sample(self, params:VyroParams, base_model, base_positive, base_negative, sampler_name, scheduler, base_ratio, denoise, refiner_detail_boost, refiner_model=None, refiner_positive=None, refiner_negative=None, latent_image=None):
if latent_image is None:
latent_image = params.latents
noise_seed = params.seed
steps = params.steps
cfg = params.cfg
cfg_method = CfgMethods.TONEMAP
dynamic_base_cfg = 0.0
dynamic_refiner_cfg = 0.0
has_refiner_model = refiner_model is not None
base_steps = int(steps * (base_ratio + 0.0001)) if has_refiner_model else steps
refiner_steps = max(0, steps - base_steps)
if denoise < 0.005:
return (params.latents,)
torch.manual_seed(params.seed)
random.seed(params.seed)
np.random.seed(params.seed)
if refiner_steps == 0 or not has_refiner_model:
result = sdxl_ksampler(base_model, None, noise_seed, base_steps, 0, cfg, sampler_name,
scheduler, base_positive, base_negative, None, None,
latent_image, denoise=denoise, disable_noise=False, start_step=0, last_step=steps,
force_full_denoise=True, dynamic_base_cfg=dynamic_base_cfg, cfg_method=cfg_method)
else:
result = sdxl_ksampler(base_model, refiner_model, noise_seed, base_steps, refiner_steps, cfg, sampler_name,
scheduler, base_positive, base_negative, refiner_positive, refiner_negative,
latent_image, denoise=denoise, disable_noise=False,
start_step=0, last_step=steps, force_full_denoise=True,
dynamic_base_cfg=dynamic_base_cfg, dynamic_refiner_cfg=dynamic_refiner_cfg,
cfg_method=cfg_method, refiner_detail_boost=refiner_detail_boost)
return result
class VyroKRestartSampler:
def __init__(self) -> None:
if 'res' not in comfy.samplers.KSampler.SAMPLERS:
comfy.samplers.KSampler.SAMPLERS.append('res')
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"params": ("VYRO_PARAMS",),
"base_model": ("MODEL",),
"base_positive": ("CONDITIONING", ),
"base_negative": ("CONDITIONING", ),
"refiner_model": ("MODEL",),
"refiner_positive": ("CONDITIONING", ),
"refiner_negative": ("CONDITIONING", ),
"sampler_name": (get_supported_samplers(), ),
"scheduler": (comfy.samplers.KSampler.SCHEDULERS, ),
"segments": ("STRING", {"default": "[3,2,0.06,0.30],[3,1,0.30,0.59]", "multiline": False}),
"restart_scheduler": (get_supported_restart_schedulers(), ),
"begin_at_step": ("INT", {"default": 1, "min": 0, "max": 10000}),
"end_at_step": ("INT", {"default": 10000, "min": 0, "max": 10000}),
"add_noise": (["enable", "disable"], ),
"return_with_leftover_noise": (["disable", "enable"], ),
"base_ratio": ("FLOAT", {"default": 0.8, "min": 0.0, "max": 1.0, "step": 0.01}),
"denoise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}),
"refiner_detail_boost": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.05},),
"cfg_clamp_after_step": ("INT", {"default": 0, "min": 0, "max": 10000}),
},
"optional": {
"refiner_prep_steps": ("INT", {"default": 0, "min": 0, "max": 10}),
"latent_image": ("LATENT",),
},
}
RETURN_TYPES = ("LATENT", "MODEL",)
FUNCTION = "sample"
CATEGORY = "Vyro/Samplers"
def sample(self, params:VyroParams, base_model, base_positive, base_negative, refiner_model, refiner_positive, refiner_negative, sampler_name, scheduler, segments, restart_scheduler, begin_at_step, end_at_step, add_noise, return_with_leftover_noise, base_ratio, denoise, refiner_detail_boost, refiner_prep_steps=0, cfg_clamp_after_step=0, latent_image=None):
force_full_denoise = True
if return_with_leftover_noise == "enable":
force_full_denoise = False
disable_noise = False
if add_noise == "disable":
disable_noise = True
steps = params.steps
base_steps = int(steps * (base_ratio + 0.0001))
refiner_steps = max(0, steps - base_steps)
if latent_image is None:
input_latent = latent_image = params.latents
else:
input_latent = latent_image
if denoise < 0.01:
return (latent_image, )
# if refiner_prep_steps is not None:
# if refiner_prep_steps >= base_steps:
# refiner_prep_steps = base_steps - 1
# if refiner_prep_steps > 0:
# start_at_step = refiner_prep_steps
# precondition_result = nodes.common_ksampler(refiner_model, params.seed + 2, steps, params.cfg, sampler_name, scheduler, refiner_positive, refiner_negative, latent_image, denoise=denoise, disable_noise=False, start_step=steps - refiner_prep_steps, last_step=steps, force_full_denoise=False)
# input_latent = precondition_result[0]
torch.manual_seed(params.seed)
random.seed(params.seed)
np.random.seed(params.seed)
if base_steps >= steps:
out = sdxl_restarts_ksampler(base_model, None, params.seed, base_steps, refiner_steps, params.cfg, sampler_name, scheduler, base_positive, base_negative, refiner_positive, refiner_negative, input_latent, segments, restart_scheduler=restart_scheduler, denoise=denoise, disable_noise=disable_noise, start_step=begin_at_step, last_step=end_at_step, force_full_denoise=force_full_denoise, refiner_detail_boost=refiner_detail_boost, cfg_clamp_after_step=cfg_clamp_after_step)[0]
# return restart_sampling(base_model, params.seed, steps, params.cfg, sampler_name, scheduler, base_positive, base_negative, input_latent, denoise, segments, restart_scheduler, begin_at_step, end_at_step, disable_noise, force_full_denoise)
return (out, base_model)
out = sdxl_restarts_ksampler(base_model, refiner_model, params.seed, base_steps, refiner_steps, params.cfg, sampler_name, scheduler, base_positive, base_negative, refiner_positive, refiner_negative, input_latent, segments, restart_scheduler=restart_scheduler, denoise=denoise, disable_noise=disable_noise, start_step=begin_at_step, last_step=end_at_step, force_full_denoise=force_full_denoise, refiner_detail_boost=refiner_detail_boost, cfg_clamp_after_step=cfg_clamp_after_step)[0]
return (out, base_model)
NODE_CLASS_MAPPINGS = {
"Vyro KRestart Sampler": VyroKRestartSampler,
"Vyro SDXL Sampler": VyroSDXLSampler,
}
NODE_DISPLAY_NAME_MAPPINGS = {
"VyroKRestartSampler": "Vyro KRestart Sampler",
"VyroSDXLSampler": "Vyro SDXL Sampler",
}
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