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import re
from itertools import product
from typing import Callable, List, Dict, Any, Union, Tuple, cast
import torch
import comfy.sample
import comfy.model_management
import comfy.samplers
from nodes import common_ksampler
from comfy.sd import ModelPatcher
from .model.iter import iterize_model, CondForModels
from .model import merge2
re_int = re.compile(r"\s*([+-]?\s*\d+)\s*")
re_float = re.compile(r"\s*([+-]?\s*\d+(?:.\d*)?)\s*")
re_range = re.compile(r"\s*([+-]?\s*\d+)\s*-\s*([+-]?\s*\d+)(?:\s*\(([+-]\d+)\s*\))?\s*")
re_range_float = re.compile(r"\s*([+-]?\s*\d+(?:.\d*)?)\s*-\s*([+-]?\s*\d+(?:.\d*)?)(?:\s*\(([+-]\d+(?:.\d*)?)\s*\))?\s*")
def frange(start, end, step):
x = float(start)
end = float(end)
step = float(step)
while x < end:
yield x
x += step
def get_noise(seeds: List[int], latent_image: torch.Tensor, disable_noise: bool, skip: int):
noises: List[torch.Tensor] = []
latents: List[torch.Tensor] = []
if latent_image.dim() == 3:
latent_image = latent_image.unsqueeze(0) # add batch dim
if disable_noise:
noise_ = torch.zeros([len(seeds)]+list(latent_image.size())[-3:], dtype=latent_image.dtype, layout=latent_image.layout, device="cpu")
noises.append(noise_)
latents.extend([latent_image] * (len(seeds) // latent_image.shape[0]))
else:
for s in seeds:
noise_ = comfy.sample.prepare_noise(latent_image, s, skip)
noises.append(noise_)
latents.append(latent_image)
return torch.cat(noises), torch.cat(latents)
def get_cfg(noises: torch.Tensor, latent_image: torch.Tensor, cfgs: List[float]):
# batch_size = noises.shape[0] * len(cfgs)
ns = [noises] * len(cfgs)
lat = [latent_image] * len(cfgs)
cf = torch.FloatTensor(cfgs * noises.shape[0])
return torch.cat(ns), torch.cat(lat), cf[...,None,None,None]
def process_cond_for_models(
cond: List[List[Union[torch.Tensor,CondForModels,dict]]],
model_index: int
):
"""
select conditioning tensor for the current model
"""
assert (
all(isinstance(p[0], CondForModels) for p in cond)
or not any(isinstance(p[0], CondForModels) for p in cond)
)
if not isinstance(cond[0][0], CondForModels):
return cond
sizes = set( len(cast(CondForModels, p[0]).ex) for p in cond )
assert len(sizes) == 1, f'number of conditions: {sizes}'
size = sizes.pop()
assert model_index < size
#
# conds
# + [ CondForModels, dictA ]
# | .ex + condA for model1
# | + condA for model2
# | ...
# | L condA for model{size}
# + [ CondForModels, dictB ]
# | .ex + condB for model1
# | + condB for model2
# | ...
# | L condB for model{size}
# ...
#
# vvv
#
# conds
# + [ [ condA_for_model1, dictA ], [ condB_for_model1, dictB ], ... ]
# + [ [ condA_for_model2, dictA ], [ condB_for_model2, dictB ], ... ] <- model_index
# ...
#
result = []
for c, *rest in cond:
assert isinstance(c, CondForModels)
actual_cond = c.ex[model_index]
result.append([actual_cond, *rest])
return result
def xyz_args(
model: ModelPatcher,
samplers: List[str],
schedulers: List[str],
steps: List[int],
):
for (model_index, model_fn), sampler, scheduler, step in product(enumerate(iterize_model(model)), samplers, schedulers, steps):
if sampler not in comfy.samplers.KSampler.SAMPLERS:
raise ValueError(f'unknown sampler name: {sampler}')
if scheduler not in comfy.samplers.KSampler.SCHEDULERS:
raise ValueError(f'unknown scheduler name: {scheduler}')
yield (
model_index,
model_fn,
step,
sampler,
scheduler,
)
def common_ksampler_xyz(
model: ModelPatcher,
seed: Union[int,List[int]],
steps: Union[int,List[int]],
cfg: Union[float,List[float]],
sampler_name: Union[str,List[str]],
scheduler: Union[str,List[str]],
positive,
negative,
latent,
denoise=1.0,
disable_noise=False,
start_step=None,
last_step=None,
force_full_denoise=False
):
if not isinstance(seed, list):
seed = [seed]
if not isinstance(steps, list):
steps = [steps]
if not isinstance(cfg, list):
cfg = [cfg]
if not isinstance(sampler_name, list):
sampler_name = [sampler_name]
if not isinstance(scheduler, list):
scheduler = [scheduler]
latent_image = latent["samples"]
noise_mask = latent.get('noise_mask', None)
noise, latent_image = get_noise(seed, latent_image, disable_noise, latent.get('batch_index', 0))
noise, latent_image, cfg_ = get_cfg(noise, latent_image, cfg)
cfg_ = cfg_.to('cuda')
all_samples: List[torch.Tensor] = []
for (
model_index, model_fn, step, sampler, scheduler
) in xyz_args(model, sampler_name, scheduler, steps):
current_model = model_fn()
positive_copy = process_cond_for_models(positive, model_index)
negative_copy = process_cond_for_models(negative, model_index)
print(f'XYZ sampler=model@{model_index}/{sampler}/{scheduler} {step}steps')
alphas = merge2.get_current_alpha(current_model.model)
if alphas is not None:
print(f'alpha = {alphas}')
samples = comfy.sample.sample(
current_model, noise, step, cfg_, sampler, scheduler,
positive_copy, negative_copy, latent_image,
denoise=denoise, disable_noise=disable_noise,
start_step=start_step, last_step=last_step,
force_full_denoise=force_full_denoise, noise_mask=noise_mask
)
samples = samples.cpu()
all_samples.append(samples)
out = latent.copy()
out["samples"] = torch.cat(all_samples)
return (out, )
class KSamplerSetting:
@classmethod
def INPUT_TYPES(cls):
return {
'required': {
'model': ('MODEL',),
'seed': ('INT', {'default': 0, 'min': 0, 'max': 0xffffffffffffffff}),
'steps': ('INT', {'default': 20, 'min': 1, 'max': 10000}),
'cfg': ('FLOAT', {'default': 8.0, 'min': 0.0, 'max': 100.0}),
'sampler_name': (comfy.samplers.KSampler.SAMPLERS, ),
'scheduler': (comfy.samplers.KSampler.SCHEDULERS, ),
'positive': ('CONDITIONING', ),
'negative': ('CONDITIONING', ),
'latent_image': ('LATENT', ),
'denoise': ('FLOAT', {'default': 1.0, 'min': 0.0, 'max': 1.0, 'step': 0.01}),
}
}
RETURN_TYPES = ('DICT',)
FUNCTION = 'sample'
CATEGORY = 'sampling'
def sample(self, **kwargs):
return kwargs,
class KSamplerOverrided:
@classmethod
def INPUT_TYPES(cls):
return {
'required': {
'setting': ('DICT',),
},
'optional': {
'model': ('MODEL',),
'seed': ('Integer', {'default': 0, 'min': 0, 'max': 0xffffffffffffffff}),
'steps': ('Integer', {'default': 20, 'min': 1, 'max': 10000}),
'cfg': ('Float', {'default': 8.0, 'min': 0.0, 'max': 100.0}),
'sampler_name': ('SamplerName',),
'scheduler': ('SchedulerName', ),
'positive': ('CONDITIONING', ),
'negative': ('CONDITIONING', ),
'latent_image': ('LATENT', ),
'denoise': ('Float', {'default': 1.0, 'min': 0.0, 'max': 1.0, 'step': 0.01}),
}
}
RETURN_TYPES = ('LATENT',)
FUNCTION = 'sample'
CATEGORY = 'sampling'
def sample(self, setting: dict, **kwargs):
if 'latent_image' in setting:
setting['latent'] = setting['latent_image']
del setting['latent_image']
setting.update(kwargs)
return common_ksampler(**setting)
class KSamplerXYZ:
@classmethod
def INPUT_TYPES(cls):
return {
'required': {
'setting': ('DICT',),
},
'optional': {
'model': ('MODEL',),
'seed': ('STRING', { 'multiline': True, 'default': '' }),
'steps': ('STRING', { 'multiline': True, 'default': '' }),
'cfg': ('STRING', { 'multiline': True, 'default': '' }),
'sampler_name': ('STRING', { 'multiline': True, 'default': '' }),
'scheduler': ('STRING', { 'multiline': True, 'default': '' }),
}
}
RETURN_TYPES = ('LATENT',)
FUNCTION = 'sample'
CATEGORY = 'sampling'
def sample(self, setting: dict, **kwargs):
if 'latent_image' in setting:
setting['latent'] = setting['latent_image']
del setting['latent_image']
# ignore empty string
kwargs = { k: v for k, v in kwargs.items() if not isinstance(v, str) or len(v) != 0 }
setting = { **setting, **kwargs }
if isinstance(setting.get('seed', None), str):
setting['seed'] = self.parse(setting['seed'], self.parse_int)
if isinstance(setting.get('steps', None), str):
setting['steps'] = self.parse(setting['steps'], self.parse_int)
if isinstance(setting.get('cfg', None), str):
setting['cfg'] = self.parse(setting['cfg'], self.parse_float)
if isinstance(setting.get('sampler_name', None), str):
setting['sampler_name'] = self.parse(setting['sampler_name'], None)
if len(setting['sampler_name']) == 1:
setting['sampler_name'] = setting['sampler_name'][0]
if isinstance(setting.get('scheduler', None), str):
setting['scheduler'] = self.parse(setting['scheduler'], None)
if len(setting['scheduler']) == 1:
setting['scheduler'] = setting['scheduler'][0]
for k, v in setting.items():
if k in kwargs and isinstance(v, (list, tuple)):
print(f'XYZ {k}: {v}')
return common_ksampler_xyz(**setting) # type: ignore
def parse(self, input: str, cont: Union[Callable[[str],Any],None]):
vs = [ x.strip() for x in input.split(',') ]
if cont is not None:
new_vs = []
for v in vs:
new_v = cont(v)
if isinstance(new_v, list):
new_vs += new_v
else:
new_vs.append(new_v)
vs = new_vs
return vs
def parse_int(self, input: str):
m = re_int.fullmatch(input)
if m is not None:
return int(m.group(1))
m = re_range.fullmatch(input)
if m is None:
raise ValueError(f'failed to process: {input}')
start, end, step = m.group(1), m.group(2), m.group(3)
if step is None:
step = 1
return list(range(int(start), int(end) + 1, int(step)))
def parse_float(self, input: str):
m = re_float.fullmatch(input)
if m is not None:
return float(m.group(1))
m = re_range_float.fullmatch(input)
if m is None:
raise ValueError(f'failed to process: {input}')
start, end, step = m.group(1), m.group(2), m.group(3)
if step is None:
step = 1.0
return list(frange(float(start), float(end), float(step)))
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