318 lines
16 KiB
Python
318 lines
16 KiB
Python
import inspect
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import logging
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import torch
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from enum import Enum, auto
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from typing import Any
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class PredictionType(Enum):
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EPS = auto() # ε-prediction
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V = auto() # v-prediction
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X0 = auto() # x₀-prediction
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UNKNOWN = auto() # couldn’t detect / new scheduler
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_RAW_TO_ENUM = {
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"eps": PredictionType.EPS,
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"epsilon": PredictionType.EPS,
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"v": PredictionType.V,
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"v_prediction": PredictionType.V,
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"x0": PredictionType.X0,
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"sample": PredictionType.X0,
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}
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class NRS:
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@classmethod
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def INPUT_TYPES(s):
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return {"required": { "model": ("MODEL",),
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"skew": ("FLOAT", {"default": 4.0, "min": -30.0, "max": 30.0, "step": 0.01}),
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"stretch": ("FLOAT", {"default": 2.0, "min": -30.0, "max": 30.0, "step": 0.01}),
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"squash": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.01}),
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}}
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RETURN_TYPES = ("MODEL",)
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FUNCTION = "patch"
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CATEGORY = "advanced/model"
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def _get_pred_type(self, model) -> PredictionType:
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"""
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In order to support Comfy, Forge, and possibly other models
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and various loaders.
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Walk common wrappers until we find something that looks like a
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prediction-type flag, then map it to the enum.
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Defaults to EPS if all else fails.
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"""
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def _canon(p):
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if p is None:
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return ""
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if isinstance(p, bytes):
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p = p.decode(errors="ignore")
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if isinstance(p, Enum):
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p = p.name
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return str(p).strip().lower()
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# Breadth-first search through a few well-known wrappers.
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queue = [model]
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visited = set()
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while queue:
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obj = queue.pop(0)
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if id(obj) in visited:
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continue
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visited.add(id(obj))
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# 1) direct hit on this object ---------------------------------
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p = _canon(getattr(obj, "parameterization", None)) # k-diffusion
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if p:
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return _RAW_TO_ENUM.get(p, PredictionType.UNKNOWN)
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p = _canon(getattr(getattr(obj, "config", None), "prediction_type", None)) # diffusers
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if p:
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return _RAW_TO_ENUM.get(p, PredictionType.UNKNOWN)
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p = _canon(getattr(obj, "prediction_type", None)) # rare misc
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if p:
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return _RAW_TO_ENUM.get(p, PredictionType.UNKNOWN)
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# 2) enqueue child containers we care about -------------------
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for attr in ("model_sampling", "model", "diffusion_model", "scheduler"):
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child = getattr(obj, attr, None)
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if child is not None:
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queue.append(child)
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# 3) default ------------------------------------------------------
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return PredictionType.EPS
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def _pre_scale_conditioning(self, x_orig, sigma, cond, uncond):
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x_div = None
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eps_cond = cond
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eps_uncond = uncond
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if self.__pred_type == PredictionType.V:
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# v → ε conversion
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logging.debug(f"NRS._pre_scale_conditioning: generating x_div, cond, and uncond for v-pred")
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sigma2_1 = (sigma ** 2 + 1.0)
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x_div = x_orig / sigma2_1
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root = sigma2_1.sqrt()
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eps_cond = ((x_div - (x_orig - cond)) * root) / (sigma)
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eps_uncond = ((x_div - (x_orig - uncond)) * root) / (sigma)
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elif self.__pred_type == PredictionType.EPS:
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logging.debug(f"NRS._pre_scale_conditioning: already in eps, no pre-scale needed")
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pass # already in ε space
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elif self.__pred_type == PredictionType.X0:
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raise NotImplementedError("NRS._pre_scale_conditioning: x0-prediction not supported yet.")
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else:
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raise RuntimeError("NRS._pre_scale_conditioning: Could not determine prediction type for this model.")
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return x_div, eps_cond, eps_uncond
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def _post_scale_conditioning(self, x_orig, x_div, x_final, sigma):
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if self.__pred_type == PredictionType.V:
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# ε → v conversion
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root = (sigma ** 2 + 1).sqrt()
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logging.debug(f"NRS._post_scale_conditioning: generating cfg_result for v-pred")
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return x_orig - (x_div - x_final * sigma / root)
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elif self.__pred_type == PredictionType.EPS:
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# already in ε space
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logging.debug(f"NRS._post_scale_conditioning: already in eps, no post-scale needed")
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return x_final
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elif self.__pred_type == PredictionType.X0:
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raise NotImplementedError("NRS._post_scale_conditioning: x0-prediction not supported yet.")
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else:
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raise RuntimeError("NRS._post_scale_conditioning: Could not determine prediction type for this model.")
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def patch(self, model, skew, stretch, squash):
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self.__pred_type = self._get_pred_type(model) if not hasattr(self, "__pred_type") else self.__pred_type
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def nrs(args):
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cond = args["cond"]
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uncond = args["uncond"]
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sigma = args["sigma"]
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sigma = sigma.view(sigma.shape[:1] + (1,) * (cond.ndim - 1))
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x_orig = args["input"]
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self.__pred_type = self.__pred_type if self.__pred_type is not None else self._get_pred_type(model)
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logging.debug(f"NRS.nrs: Skew: {skew}, Stretch: {stretch}, Squash: {squash}")
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x_div, eps_cond, eps_uncond = self._pre_scale_conditioning(x_orig, sigma, cond, uncond)
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x_final = None
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match "v0.5.0":
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case "v1":
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# displace cond by rejection of uncond on cond
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u_dot_c = torch.sum(eps_uncond * eps_cond, dim=-1, keepdim=True)
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c_dot_c = torch.sum(eps_cond * eps_cond, dim=-1, keepdim=True)
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u_on_c = (u_dot_c / c_dot_c) * eps_cond
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u_rej_c = eps_uncond - u_on_c
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displaced = (eps_cond - skew * u_rej_c)
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logging.debug(f"NRS.nrs: displaced")
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# squash displaced vector towards len(cond) based on squash scale
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d_len_sq = torch.sum(displaced * displaced, dim=-1, keepdim=True)
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squash_scale = (1 - squash) + squash * ((c_dot_c/d_len_sq) ** 0.5)
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squashed = displaced * squash_scale
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logging.debug(f"NRS.nrs: squashed")
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# stretch turned vector towards cond based on stretch scale
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sq_dot_c = torch.sum(squashed * eps_cond, dim=-1, keepdim=True)
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sq_on_c = (sq_dot_c / c_dot_c) * eps_cond
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x_final = squashed + sq_on_c * stretch
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logging.debug(f"NRS.nrs: final")
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case "v2":
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# displace cond by rejection of uncond on cond
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u_dot_c = torch.sum(eps_uncond * eps_cond, dim=-1, keepdim=True)
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c_dot_c = torch.sum(eps_cond * eps_cond, dim=-1, keepdim=True)
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u_on_c_mag = (u_dot_c / c_dot_c)
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u_on_c = u_on_c_mag * eps_cond
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u_rej_c = eps_uncond - u_on_c
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displaced = eps_cond + stretch * (eps_cond - torch.clamp(u_dot_c / c_dot_c, min=0, max=1) * eps_cond) - skew * u_rej_c
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logging.debug(f"NRS.nrs: displaced & stretched")
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# squash displaced vector towards len(cond) based on squash scale
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d_len_sq = torch.sum(displaced * displaced, dim=-1, keepdim=True)
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squash_scale = (1 - squash) + squash * ((c_dot_c/d_len_sq) ** 0.5)
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x_final = displaced * squash_scale
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logging.debug(f"NRS.nrs: final")
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case "v3":
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# displace cond by rejection of uncond on cond
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u_dot_c = torch.sum(eps_uncond * eps_cond, dim=-1, keepdim=True)
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c_dot_c = torch.sum(eps_cond * eps_cond, dim=-1, keepdim=True)
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u_on_c_mag = (u_dot_c / c_dot_c)
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u_on_c = u_on_c_mag * eps_cond
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u_rej_c = eps_uncond - u_on_c
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displaced = (eps_cond - skew * u_rej_c)
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logging.debug(f"NRS.nrs: displaced")
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# squash displaced vector towards len(cond) based on squash scale
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d_len_sq = torch.sum(displaced * displaced, dim=-1, keepdim=True)
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squash_scale = (1 - squash) + squash * ((c_dot_c/d_len_sq) ** 0.5)
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# stretch vector towards 2*len(cond) - len(u_on_c)
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c_len = c_dot_c ** 0.5
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stretch_scale = (1 - stretch) + stretch * (2 * c_len - u_on_c_mag)/c_len
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x_final = displaced * squash_scale * stretch_scale
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logging.debug(f"NRS.nrs: final")
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case "v4":
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u_dot_c = torch.sum(eps_uncond * eps_cond, dim=-1, keepdim=True)
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c_dot_c = torch.sum(eps_cond * eps_cond, dim=-1, keepdim=True)
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u_on_c_mag = (u_dot_c / c_dot_c)
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u_on_c = u_on_c_mag * eps_cond
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u_rej_c = eps_uncond - u_on_c
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rej_dor_rej = torch.sum(u_rej_c * u_rej_c, dim=-1, keepdim=True)
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x_final = (eps_cond - squash * u_rej_c + stretch * eps_cond * ((rej_dor_rej/c_dot_c) ** 0.5))
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logging.debug(f"NRS.nrs: displaced")
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case "v0.4.1":
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u_dot_c = torch.sum(eps_uncond * eps_cond, dim=-1, keepdim=True)
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c_dot_c = torch.sum(eps_cond * eps_cond, dim=-1, keepdim=True)
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u_on_c_mag = (u_dot_c / c_dot_c)
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u_on_c = u_on_c_mag * eps_cond
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u_rej_c = eps_uncond - u_on_c
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rej_dor_rej = torch.sum(u_rej_c * u_rej_c, dim=-1, keepdim=True)
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stretched = eps_cond + stretch * eps_cond * ((rej_dor_rej/c_dot_c) ** 0.5)
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skewed = stretched - skew * u_rej_c
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sk_dot_sk = torch.sum(skewed * skewed, dim=-1, keepdim=True)
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squash_scale = (1 - squash) + squash * ((c_dot_c/sk_dot_sk) ** 0.5)
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x_final = skewed * squash_scale
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logging.debug(f"NRS.nrs: displaced")
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case "v0.4.2":
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u_dot_c = torch.sum(eps_uncond * eps_cond, dim=-1, keepdim=True)
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c_dot_c = torch.sum(eps_cond * eps_cond, dim=-1, keepdim=True)
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u_on_c_mag = (u_dot_c / c_dot_c)
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u_on_c = u_on_c_mag * eps_cond
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u_rej_c = eps_uncond - u_on_c
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proj_len = torch.sum(u_on_c * u_on_c, dim=-1, keepdim=True) ** 0.5
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cond_len = c_dot_c ** 0.5
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stretched = eps_cond * (1 + stretch * torch.abs(cond_len - proj_len) / cond_len)
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skewed = stretched - skew * u_rej_c
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sk_dot_sk = torch.sum(skewed * skewed, dim=-1, keepdim=True)
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squash_scale = (1 - squash) + squash * cond_len / (sk_dot_sk ** 0.5)
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x_final = skewed * squash_scale
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logging.debug(f"NRS.nrs: displaced")
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case "v0.4.3":
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u_dot_c = torch.sum(eps_uncond * eps_cond, dim=-1, keepdim=True)
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c_dot_c = torch.sum(eps_cond * eps_cond, dim=-1, keepdim=True)
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u_on_c_mag = (u_dot_c / c_dot_c)
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u_on_c = u_on_c_mag * eps_cond
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u_rej_c = eps_uncond - u_on_c
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proj_len = torch.sum(u_on_c * u_on_c, dim=-1, keepdim=True) ** 0.5
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cond_len = c_dot_c ** 0.5
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stretched = eps_cond * (1 + stretch * (cond_len - proj_len) / cond_len)
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skewed = stretched - skew * u_rej_c
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sk_dot_sk = torch.sum(skewed * skewed, dim=-1, keepdim=True)
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squash_scale = (1 - squash) + squash * cond_len / (sk_dot_sk ** 0.5)
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x_final = skewed * squash_scale
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logging.debug(f"NRS.nrs: displaced")
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case "v0.4.4":
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u_dot_c = torch.sum(eps_uncond * eps_cond, dim=-1, keepdim=True)
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c_dot_c = torch.sum(eps_cond * eps_cond, dim=-1, keepdim=True)
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u_on_c_mag = (u_dot_c / c_dot_c)
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u_on_c = u_on_c_mag * eps_cond
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u_rej_c = eps_uncond - u_on_c
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cond_len = c_dot_c ** 0.5
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proj_diff = eps_cond - u_on_c
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proj_diff_len = torch.sum(proj_diff * proj_diff, dim=-1, keepdim=True) ** 0.5
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stretched = eps_cond * (1 + stretch * proj_diff_len / cond_len)
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skewed = stretched - skew * u_rej_c
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sk_dot_sk = torch.sum(skewed * skewed, dim=-1, keepdim=True)
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squash_scale = (1 - squash) + squash * cond_len / (sk_dot_sk ** 0.5)
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x_final = skewed * squash_scale
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logging.debug(f"NRS.nrs: displaced")
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case "v0.4.5":
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u_dot_c = torch.sum(eps_uncond * eps_cond, dim=-1, keepdim=True)
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c_dot_c = torch.sum(eps_cond * eps_cond, dim=-1, keepdim=True)
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u_on_c_mag = (u_dot_c / c_dot_c)
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u_on_c = u_on_c_mag * eps_cond
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u_rej_c = eps_uncond - u_on_c
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cond_len = c_dot_c ** 0.5
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proj_diff = eps_cond - u_on_c
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# Amplify Cond based on length compared to projection of uncond
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stretched = eps_cond + (stretch * proj_diff)
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# Skew/Steer Conf based on rejection of uncond on cond
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skewed = stretched - skew * u_rej_c
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# Squash final length back down to original length of cond
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sk_dot_sk = torch.sum(skewed * skewed, dim=-1, keepdim=True)
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squash_scale = (1 - squash) + squash * cond_len / (sk_dot_sk ** 0.5)
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x_final = skewed * squash_scale
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case "v0.5.0":
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def _dot(a, b):
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return (a*b).flatten(1).sum(dim=1, keepdim=True) # [B,1]
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def _nrm2(v):
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return _dot(v, v)
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eps = eps = torch.finfo(eps_cond.dtype).eps
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c_dot_c = _nrm2(eps_cond) + eps # [B,1]
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u_dot_c = _dot(eps_uncond, eps_cond) # [B,1]
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u_on_c = (u_dot_c / c_dot_c).view(-1, 1, 1, 1) * eps_cond # [B,1,1,1] * [B,C,H,W]
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u_rej_c = eps_uncond - u_on_c
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proj_diff = eps_cond - u_on_c
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# Amplify Cond based on length compared to projection of uncond
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stretched = eps_cond + (stretch * proj_diff)
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# Skew/Steer Conf based on rejection of uncond on cond
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skewed = stretched - skew * u_rej_c
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# Squash final length back down to original length of cond
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cond_len = torch.sqrt(c_dot_c) # [B,1]
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nrs_len = torch.sqrt(_nrm2(skewed) + eps) # [B,1]
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squash_scale = (1 - squash) + squash * (cond_len / nrs_len)
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x_final = skewed * squash_scale.view(-1, 1, 1, 1)
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return self._post_scale_conditioning(x_orig, x_div, x_final, sigma)
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m = model.clone()
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m.set_model_sampler_cfg_function(nrs, True)
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return (m, )
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NODE_CLASS_MAPPINGS = {
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"NRS": NRS,
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} |