blepping
d8574bae2d
* Weird experiments with sampler blending * Mega sync * Use ComfyUI union types for wildcard inputs when available * Add weoon wavelet sampler * Fix noise sampler caching not considering immiscible settings Allow specifying alt custom noise/immiscible settings for samplers that internally add noise AFS support at the merge sampler level Allow the Weoon internal step to use ETA Add t_copysign expression function * Start updating documentation * Add ImmiscibleReference node, other changes * Respect factor in ImmiscibleReference noise * Fixing normalizing in ImmiscibleReference noise * Handle case where there are no RGB factors (i.e. audio models) * Fix rectified flow type detection for non-Flux models * Add more tensor ops, add OCS ApplyExpressionImage/Latent nodes * Rename ApplyExpression nodes to ApplyFilter, expression QoL improvements * Fix apply filter nodes definitions * Make t_noise handler work for image-latents. * Add t_new_like expression function * Make it possible to comment out lines with hash in expressions * Add gradient estimation, pingpong and res_multistep samplers. Add pingpong group merge method. Remove model call caching stuff. Allow defining pre_cfg and post_cfg filters. Handle filters changing the latent shape better. Allow disabling detecting out of order sigmas as restart sampling. * Sync current updates (which I am too lazy to describe individually) * Add ExpressionFilteredNoise node Improvements to immiscible noise handling/expanded features
357 lines
11 KiB
Python
357 lines
11 KiB
Python
import torch
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import comfy
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from comfy.k_diffusion.sampling import to_d
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from . import filtering
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from .utils import fallback
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from .latent import OCSLatentFormat
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class History:
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def __init__(self, size):
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self.history = []
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self.size = size
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def __len__(self):
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return len(self.history)
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def __getitem__(self, k):
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return self.history[k]
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def push(self, val):
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if len(self.history) >= self.size:
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self.history = self.history[-(self.size - 1) :]
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self.history.append(val)
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def reset(self):
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self.history = []
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def clone(self):
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obj = self.__new__(self.__class__)
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obj.__init__(self.size)
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obj.history = self.history.copy()
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return obj
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class ModelResult:
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def __init__(
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self,
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call_idx,
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sigma,
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x,
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denoised,
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have_uncond=True,
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**kwargs,
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):
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self.call_idx = call_idx
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self.sigma = sigma
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self.x = x
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self.denoised = denoised
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self.have_uncond = have_uncond
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for k in ("denoised_uncond", "denoised_cond", "tangents", "jdenoised"):
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setattr(self, k, kwargs.pop(k, None))
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if len(kwargs) != 0:
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raise ValueError(f"Unexpected keyword arguments: {tuple(kwargs.keys())}")
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def to_d(
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self,
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/,
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x=None,
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sigma=None,
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denoised=None,
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denoised_uncond=None,
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alt_cfgpp_scale=0,
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cfgpp=False,
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):
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x = fallback(x, self.x)
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sigma = fallback(sigma, self.sigma)
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denoised = fallback(denoised, self.denoised)
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denoised_uncond = fallback(denoised_uncond, self.denoised_uncond)
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if alt_cfgpp_scale != 0:
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x = x - denoised * alt_cfgpp_scale + denoised_uncond * alt_cfgpp_scale
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return to_d(x, sigma, denoised if not cfgpp else denoised_uncond)
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def get_split_prediction(
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self,
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*,
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x=None,
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d=None,
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sigma=None,
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denoised=None,
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denoised_uncond=None,
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alt_cfgpp_scale=0,
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cfgpp=False,
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):
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denoised = fallback(denoised, self.denoised)
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denoised_uncond = fallback(denoised_uncond, self.denoised_uncond)
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x = fallback(x, self.x)
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sigma = fallback(sigma, self.sigma)
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if d is None:
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d = self.to_d(
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x=x,
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sigma=sigma,
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denoised=denoised,
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denoised_uncond=denoised_uncond,
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alt_cfgpp_scale=alt_cfgpp_scale,
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cfgpp=cfgpp,
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)
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denoised_pred = denoised if alt_cfgpp_scale == 0 else x - d * sigma
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return (denoised_pred, d)
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@property
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def d(self):
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return self.to_d()
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def clone(self, deep=False):
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obj = self.__new__(self.__class__)
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for k in (
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"denoised",
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"call_idx",
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"sigma",
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"x",
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"denoised_uncond",
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"denoised_cond",
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"tangents",
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"jdenoised",
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):
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val = getattr(self, k)
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if deep and isinstance(val, torch.Tensor):
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val = val.copy()
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setattr(obj, k, val)
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return obj
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def get_error(self, other, *, override=None, alt_cfgpp_scale=0, cfgpp=False):
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slf = fallback(override, self)
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first, second = (other, slf) if other.sigma > slf.sigma else (slf, other)
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if first.sigma == second.sigma:
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return 0.0
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d = first.to_d(alt_cfgpp_scale=alt_cfgpp_scale, cfgpp=cfgpp)
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d_pred = second.to_d(
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x=first.x + d * (second.sigma - first.sigma),
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alt_cfgpp_scale=alt_cfgpp_scale,
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cfgpp=cfgpp,
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)
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return torch.linalg.norm(d_pred.sub_(d)).div_(torch.linalg.norm(d)).item()
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class OCSModel:
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def __init__(
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self,
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model,
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x: torch.Tensor,
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s_in: torch.Tensor,
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extra_args: dict,
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*,
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cache: dict | None = None,
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filter: dict | None = None,
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cfg1_uncond_optimization: bool = False,
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cfg_scale_override: int | float | None = None,
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) -> None:
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filtargs = fallback(filter, {}).copy()
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self.filters = {}
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for key in (
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"input",
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"denoised",
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"jdenoised",
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"cond",
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"uncond",
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"postcfg",
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"precfg",
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):
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filt = filtargs.pop(key, None)
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if filt is None:
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continue
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self.filters[key] = filtering.make_filter(filt)
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self.model = model
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self.s_in = s_in
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self.extra_args = extra_args
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self.cfg1_uncond_optimization = cfg1_uncond_optimization
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self.cfg_scale_override = cfg_scale_override
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self.is_rectified_flow = isinstance(
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model.inner_model.inner_model.model_sampling, comfy.model_sampling.CONST
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)
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self.latent_format = OCSLatentFormat(
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x.device, model.inner_model.inner_model.latent_format
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)
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def maybe_filter(
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self, name: str, latent: torch.Tensor, *args: list, **kwargs: dict
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) -> torch.Tensor:
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filt = self.filters.get(name)
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if filt is None:
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return latent
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return filt.apply(latent, *args, **kwargs)
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def filter_result(
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self, result: ModelResult, *args: list, **kwargs: dict
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) -> ModelResult:
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if not self.filters:
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return result
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result = result.clone()
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for key in ("denoised", "cond", "uncond", "jdenoised"):
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filt = self.filters.get(key)
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if filt is None:
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continue
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attk = f"denoised_{key}" if key in {"cond", "uncond"} else key
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inpval = getattr(result, attk, None)
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if inpval is None:
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continue
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setattr(result, attk, filt.apply(inpval, *args, **kwargs))
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return result
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@staticmethod
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def _fr_add_mr(fr: filtering.FilterRefs, mr: ModelResult) -> filtering.FilterRefs:
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frmr = filtering.FilterRefs.from_mr(mr)
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fr.kvs |= {f"{k}_curr": v for k, v in frmr.kvs.items()}
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return fr
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def call_model(
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self, x: torch.Tensor, sigma: torch.Tensor, **kwargs: dict
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) -> torch.Tensor:
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return self.model(x, sigma * self.s_in, **self.extra_args | kwargs)
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@property
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def model_sampling(self):
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return self.model.inner_model.inner_model.model_sampling
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@property
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def inner_cfg_scale(self) -> None | int | float:
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maybe_cfg_scale = getattr(self.model.inner_model, "cfg", None)
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return maybe_cfg_scale if isinstance(maybe_cfg_scale, (int, float)) else None
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def set_inner_cfg_scale(self, scale: None | int | float) -> None | int | float:
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eff_scale = self.cfg_scale_override
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if scale is not None:
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eff_scale = None if scale < 0 else scale
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if eff_scale is None or eff_scale < 0:
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return None
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curr_cfg_scale = self.inner_cfg_scale
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if curr_cfg_scale is None:
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return None
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self.model.inner_model.cfg = eff_scale
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return curr_cfg_scale
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def __call__(
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self,
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x: torch.Tensor,
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sigma: torch.Tensor,
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*,
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call_index: int = 0,
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ss,
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s_in=None,
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tangents=None,
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require_uncond: bool = False,
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cfg_scale_override: None | int = None,
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**kwargs,
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) -> ModelResult:
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filter_refs = ss.refs | filtering.FilterRefs({
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"model_call": call_index,
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"orig_x": x,
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})
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comfy.model_management.throw_exception_if_processing_interrupted()
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model_options = self.extra_args.get("model_options", {}).copy()
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denoised_cond = denoised_uncond = None
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have_uncond = True
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def postcfg(args):
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nonlocal denoised_cond, denoised_uncond, have_uncond
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denoised_uncond = args["uncond_denoised"]
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denoised_cond = args["cond_denoised"]
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result = args["denoised"]
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if "postcfg" in self.filters:
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result = self.maybe_filter(
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"postcfg",
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result,
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refs=filter_refs
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| filtering.FilterRefs({
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"postcfg_input": args["input"],
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"postcfg_sigma": args["sigma"],
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"postcfg_denoised_cond": denoised_cond,
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"postcfg_denoised_uncond": denoised_uncond,
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}),
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)
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if denoised_uncond is None:
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have_uncond = False
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denoised_uncond = denoised_cond
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return result
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def precfg(args):
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conds_out = args["conds_out"]
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precfg_refs = (
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filter_refs
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| filtering.FilterRefs({
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"precfg_input": args["input"],
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"precfg_sigma": args["sigma"],
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"precfg_cond_scale": args["cond_scale"],
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})
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| filtering.FilterRefs({
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f"precfg_cond_{idx}": cond for idx, cond in enumerate(conds_out)
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})
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)
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return [
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self.maybe_filter(
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"precfg",
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curr_cond,
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refs=precfg_refs | filtering.FilterRefs({"cond_idx": cond_idx}),
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)
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for cond_idx, curr_cond in enumerate(conds_out)
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]
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orig_cfg_scale = self.set_inner_cfg_scale(cfg_scale_override)
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model_options = comfy.model_patcher.set_model_options_post_cfg_function(
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model_options,
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postcfg,
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disable_cfg1_optimization=require_uncond
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or not self.cfg1_uncond_optimization,
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)
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if "precfg" in self.filters:
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model_options = comfy.model_patcher.set_model_options_pre_cfg_function(
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model_options,
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precfg,
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)
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extra_args = self.extra_args | {"model_options": model_options}
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s_in = fallback(s_in, self.s_in)
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if s_in.shape[0] != x.shape[0]:
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s_in = self.s_in = x.new_ones((x.shape[0],))
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x = self.maybe_filter("input", x, refs=filter_refs)
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def call_model(x, sigma, **kwargs):
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return self.model(x, sigma * s_in, **extra_args | kwargs)
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if tangents is None:
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denoised = call_model(x, sigma, **kwargs)
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self.set_inner_cfg_scale(orig_cfg_scale)
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mr = ModelResult(
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call_index,
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sigma,
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x,
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denoised,
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have_uncond=have_uncond,
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denoised_uncond=denoised_uncond,
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denoised_cond=denoised_cond,
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)
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self._fr_add_mr(filter_refs, mr)
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mr = self.filter_result(mr, default_ref=x, refs=filter_refs)
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return mr
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denoised, denoised_prime = torch.func.jvp(call_model, (x, sigma), tangents)
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self.set_inner_cfg_scale(orig_cfg_scale)
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mr = ModelResult(
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call_index,
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sigma,
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x,
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denoised,
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have_uncond=have_uncond,
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jdenoised=denoised_prime,
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denoised_uncond=denoised_uncond,
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denoised_cond=denoised_cond,
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)
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self._fr_add_mr(filter_refs, mr)
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mr = self.filter_result(mr, default_ref=x, refs=filter_refs)
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return mr
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