Cleanup old math versions and fix variables (#16 )

Update README.md
2025-07-26 04:34:34 -07:00 · 2025-07-21 04:01:10 -07:00 · 2025-07-21 04:00:01 -07:00 · 2025-07-21 03:59:41 -07:00 · 2025-07-21 03:56:18 -07:00 · 2025-07-21 03:50:11 -07:00
14 changed files with 323 additions and 178 deletions
@@ -0,0 +1,27 @@
+name: Publish to Comfy registry
+on:
+  workflow_dispatch:
+  push:
+    branches:
+      - main
+      - master
+    paths:
+      - "pyproject.toml"
+
+permissions:
+  issues: write
+
+jobs:
+  publish-node:
+    name: Publish Custom Node to registry
+    runs-on: ubuntu-latest
+    if: ${{ github.repository_owner == 'Reithan' }}
+    steps:
+      - name: Check out code
+        uses: actions/checkout@v4
+        with:
+          submodules: true
+      - name: Publish Custom Node
+        uses: Comfy-Org/publish-node-action@v1
+        with:
+          personal_access_token: ${{ secrets.COMFY_REGISTRY_KEY }}
@@ -1,179 +1,216 @@
-import ldm_patched.modules.model_base
 import logging
 import torch
+from enum import Enum, auto
+from typing import Any
+
+
+class PredictionType(Enum):
+    EPS     = auto()   # ε-prediction
+    V       = auto()   # v-prediction
+    X0      = auto()   # x₀-prediction
+    UNKNOWN = auto()   # couldn’t detect / new scheduler
+
+_RAW_TO_ENUM = {
+    "eps":          PredictionType.EPS,
+    "epsilon":      PredictionType.EPS,
+    "v":            PredictionType.V,
+    "v_prediction": PredictionType.V,
+    "x0":           PredictionType.X0,
+    "sample":       PredictionType.X0,
+}

 class NRS:
    @classmethod
    def INPUT_TYPES(s):
        return {"required": { "model": ("MODEL",),
-                              "skew": ("FLOAT", {"default": 2.0, "min": -30.0, "max": 30.0, "step": 0.01}),
+                              "skew": ("FLOAT", {"default": 4.0, "min": -30.0, "max": 30.0, "step": 0.01}),
                              "stretch": ("FLOAT", {"default": 2.0, "min": -30.0, "max": 30.0, "step": 0.01}),
-                              "squash": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}),
+                              "squash": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.01}),
                              }}
    RETURN_TYPES = ("MODEL",)
    FUNCTION = "patch"

    CATEGORY = "advanced/model"

+    def _get_pred_type(self, model) -> PredictionType:
+        """
+        In order to support Comfy, Forge, and possibly other models
+        and various loaders.
+        Walk common wrappers until we find something that looks like a
+        prediction-type flag, then map it to the enum.
+        Defaults to EPS if all else fails.
+        """
+        def _canon(p):
+            if p is None:
+                return ""
+            if isinstance(p, bytes):
+                p = p.decode(errors="ignore")
+            if isinstance(p, Enum):
+                p = p.name
+            return str(p).strip().lower()
+
+        # Breadth-first search through a few well-known wrappers.
+        queue, seen = [model], set()
+
+        while queue:
+            obj = queue.pop(0)
+
+            # 1) direct hit on this object ---------------------------------
+            for attr in ("model_type", "prediction_type", "parameterization"):
+                p = _canon(getattr(obj, attr, None))
+                if p:
+                    return _RAW_TO_ENUM.get(p, PredictionType.UNKNOWN)
+
+            # 2) enqueue child containers we care about -------------------
+            for attr in ("model", "diffusion_model", "config", "scheduler", "inner_model", "model_sampling"):
+                child = getattr(obj, attr, None)
+                if child is not None and id(child) not in seen:
+                    seen.add(id(child))
+                    queue.append(child)
+
+        # 3) default ------------------------------------------------------
+        return PredictionType.UNKNOWN
+   
+    def _convert_to_eps_space(self, x_orig, sig_root, sigma, cond, uncond):
+        x_div = None
+        eps_cond = cond
+        eps_uncond = uncond
+        if self.__pred_type == PredictionType.V:
+            # v → ε conversion
+            logging.debug(f"NRS._convert_to_eps_space: generating x_div, eps_cond, and eps_uncond for v-pred")
+            x_div = x_orig / (sigma ** 2 + 1)
+
+            eps_cond = ((x_div - (x_orig - cond)) * sig_root) / (sigma)
+            eps_uncond = ((x_div - (x_orig - uncond)) * sig_root) / (sigma)
+        elif self.__pred_type == PredictionType.EPS:
+            logging.debug(f"NRS._convert_to_eps_space: already in eps, no pre-scale needed")
+            pass  # already in ε space
+        elif self.__pred_type == PredictionType.X0:
+            raise NotImplementedError("NRS._convert_to_eps_space: x0-prediction not supported yet.")
+        else:
+            raise RuntimeError("NRS._convert_to_eps_space: Could not determine prediction type for this model.")
+        
+        return x_div, eps_cond, eps_uncond
+
+    def _finalize_from_eps_space(self, x_orig, x_div, x_final, sig_root, sigma):
+        nrs_result = x_final
+        if self.__pred_type == PredictionType.V:
+            # ε → v conversion
+            logging.debug(f"NRS._finalize_from_eps_space: generating cfg_result for v-pred")
+            nrs_result = x_orig - (x_div - x_final * sigma / sig_root)
+        elif self.__pred_type == PredictionType.EPS:
+            # already in ε space
+            logging.debug(f"NRS._finalize_from_eps_space: already in eps, no post-scale needed")
+            pass
+        elif self.__pred_type == PredictionType.X0:
+            raise NotImplementedError("NRS._finalize_from_eps_space: x0-prediction not supported yet.")
+        else:
+            raise RuntimeError("NRS._finalize_from_eps_space: Could not determine prediction type for this model.")
+        return nrs_result
+      
+    def _convert_to_v_space(self, x_orig, sig_root, sigma, cond, uncond):
+        x_div = None
+        v_cond = cond
+        v_uncond = uncond
+        if self.__pred_type == PredictionType.V:
+            logging.debug(f"NRS._convert_to_v_space: already in v, no pre-scale needed")
+            pass  # already in v space
+        elif self.__pred_type == PredictionType.EPS:
+            # ε → v conversion
+            logging.debug(f"NRS._convert_to_v_space: generating x_div, v_cond, and v_uncond for eps")
+            x_div = x_orig / (sigma ** 2 + 1)
+            factor = sigma / sig_root
+
+            v_cond = x_orig - (x_div - cond * factor)
+            v_uncond = x_orig - (x_div - uncond * factor)
+        elif self.__pred_type == PredictionType.X0:
+            raise NotImplementedError("NRS._convert_to_v_space: x0-prediction not supported yet.")
+        else:
+            raise RuntimeError("NRS._convert_to_v_space: Could not determine prediction type for this model.")
+        
+        return x_div, v_cond, v_uncond
+
+    def _finalize_from_v_space(self, x_orig, x_div, x_final, sig_root, sigma):
+        nrs_result = x_final
+        if self.__pred_type == PredictionType.V:
+            # already in v space
+            logging.debug(f"NRS._finalize_from_v_space: already in v, no post-scale needed")
+            pass
+        elif self.__pred_type == PredictionType.EPS:
+            # v → ε conversion
+            logging.debug(f"NRS._finalize_from_v_space: generating cfg_result for eps")
+            nrs_result = (x_div - (x_orig - x_final)) * (sig_root / sigma)
+        elif self.__pred_type == PredictionType.X0:
+            raise NotImplementedError("NRS._finalize_from_v_space: x0-prediction not supported yet.")
+        else:
+            raise RuntimeError("NRS._finalize_from_v_space: Could not determine prediction type for this model.")
+        return nrs_result
+        
    def patch(self, model, skew, stretch, squash):
+        self.__pred_type = self._get_pred_type(model) if not hasattr(self, "__pred_type") else self.__pred_type
+        self.__OPERATION_SPACE = PredictionType.V
+
        def nrs(args):
+            logging.debug(f"NRS.nrs: Skew: {skew}, Stretch: {stretch}, Squash: {squash}")
+            # self.__pred_type = self.__pred_type if self.__pred_type is not None else self._get_pred_type(model)
            cond = args["cond"]
            uncond = args["uncond"]
+            x_orig = args["input"]
+            
            sigma = args["sigma"]
            sigma = sigma.view(sigma.shape[:1] + (1,) * (cond.ndim - 1))
-            x_orig = args["input"]
+            sig_root = (sigma ** 2 + 1).sqrt()
+            
+            x_div, nrs_cond, nrs_uncond = None, None, None
+            match self.__OPERATION_SPACE:
+                case PredictionType.V:
+                    x_div, nrs_cond, nrs_uncond = self._convert_to_v_space(x_orig, sig_root, sigma, cond, uncond)
+                case PredictionType.EPS:
+                    x_div, nrs_cond, nrs_uncond = self._convert_to_eps_space(x_orig, sig_root, sigma, cond, uncond)
+                case PredictionType.X0:
+                    raise RuntimeError("NRS.nrs: x0-prediction not supported yet.")
+                case PredictionType.UNKNOWN:
+                    raise RuntimeError("NRS.nrs: Could not determine prediction type for this operation.")
+                case _:
+                    raise RuntimeError("NRS.nrs: Invalid PredictionType used.")

-            logging.debug(f"NRS.nrs: Skew: {skew}, Stretch: {stretch}, Squash: {squash}")
+            def _dot(a, b):
+                return (a*b).sum(dim=1, keepdim=True) # [B,C,W,H] => [B,1,W,H]

-            #rescale cfg has to be done on v-pred model output
-            x = x_orig / (sigma * sigma + 1.0)
-            cond = ((x - (x_orig - cond)) * (sigma ** 2 + 1.0) ** 0.5) / (sigma)
-            uncond = ((x - (x_orig - uncond)) * (sigma ** 2 + 1.0) ** 0.5) / (sigma)
-            logging.debug(f"NRS.nrs: generated cond and uncond")
+            def _nrm2(v):
+                return _dot(v, v)

-            x_final = None
-            match "v0.4.5":
-                case "v1":
-                    # displace cond by rejection of uncond on cond
-                    u_dot_c = torch.sum(uncond * cond, dim=-1, keepdim=True)
-                    c_dot_c = torch.sum(cond * cond, dim=-1, keepdim=True)
-                    u_on_c = (u_dot_c / c_dot_c) * cond
-                    u_rej_c = uncond - u_on_c
-                    displaced = (cond - skew * u_rej_c)
-                    logging.debug(f"NRS.nrs: displaced")
+            eps = torch.finfo(nrs_cond.dtype).eps
+            c_dot_c = _nrm2(nrs_cond) + eps # [B,1,W,H]
+            u_dot_c = _dot(nrs_uncond, nrs_cond) # [B,1,W,H]
+            u_on_c = (u_dot_c / c_dot_c) * nrs_cond # [B,1,W,H] * [B,C,H,W]
+            
+            # Amplify Cond based on length compared to projection of uncond
+            proj_diff = nrs_cond - u_on_c
+            stretched = nrs_cond + (stretch * proj_diff)

-                    # squash displaced vector towards len(cond) based on squash scale
-                    d_len_sq = torch.sum(displaced * displaced, dim=-1, keepdim=True)
-                    squash_scale = (1 - squash) + squash * ((c_dot_c/d_len_sq) ** 0.5)
-                    squashed = displaced * squash_scale
-                    logging.debug(f"NRS.nrs: squashed")
+            # Skew/Steer Conf based on rejection of uncond on cond
+            u_rej_c = nrs_uncond - u_on_c
+            skewed = stretched - (skew * u_rej_c)

-                    # stretch turned vector towards cond based on stretch scale
-                    sq_dot_c = torch.sum(squashed * cond, dim=-1, keepdim=True)
-                    sq_on_c = (sq_dot_c / c_dot_c) * cond
-                    x_final = squashed + sq_on_c * stretch
-                    logging.debug(f"NRS.nrs: final")
-                case "v2":
-                    # displace cond by rejection of uncond on cond
-                    u_dot_c = torch.sum(uncond * cond, dim=-1, keepdim=True)
-                    c_dot_c = torch.sum(cond * cond, dim=-1, keepdim=True)
-                    u_on_c_mag = (u_dot_c / c_dot_c)
-                    u_on_c = u_on_c_mag * cond
-                    u_rej_c = uncond - u_on_c
-                    displaced = cond + stretch * (cond - torch.clamp(u_dot_c / c_dot_c, min=0, max=1) * cond) - skew * u_rej_c
-                    logging.debug(f"NRS.nrs: displaced & stretched")
+            # Squash final length back down to original length of cond
+            cond_len = nrs_cond.norm(dim=1, keepdim=True)
+            nrs_len = skewed.norm(dim=1, keepdim=True) + eps

-                    # squash displaced vector towards len(cond) based on squash scale
-                    d_len_sq = torch.sum(displaced * displaced, dim=-1, keepdim=True)
-                    squash_scale = (1 - squash) + squash * ((c_dot_c/d_len_sq) ** 0.5)
-                    x_final = displaced * squash_scale
-                    logging.debug(f"NRS.nrs: final")
-                case "v3":
-                    # displace cond by rejection of uncond on cond
-                    u_dot_c = torch.sum(uncond * cond, dim=-1, keepdim=True)
-                    c_dot_c = torch.sum(cond * cond, dim=-1, keepdim=True)
-                    u_on_c_mag = (u_dot_c / c_dot_c)
-                    u_on_c = u_on_c_mag * cond
-                    u_rej_c = uncond - u_on_c
-                    displaced = (cond - skew * u_rej_c)
-                    logging.debug(f"NRS.nrs: displaced")
+            squash_scale = (1 - squash) + (squash * (cond_len / nrs_len))
+            x_final = skewed * squash_scale

-                    # squash displaced vector towards len(cond) based on squash scale
-                    d_len_sq = torch.sum(displaced * displaced, dim=-1, keepdim=True)
-                    squash_scale = (1 - squash) + squash * ((c_dot_c/d_len_sq) ** 0.5)
-
-                    # stretch vector towards 2*len(cond) - len(u_on_c)
-                    c_len = c_dot_c ** 0.5
-                    stretch_scale = (1 - stretch) + stretch * (2 * c_len - u_on_c_mag)/c_len
-
-                    x_final = displaced * squash_scale * stretch_scale
-                    logging.debug(f"NRS.nrs: final")
-                case "v4":
-                    u_dot_c = torch.sum(uncond * cond, dim=-1, keepdim=True)
-                    c_dot_c = torch.sum(cond * cond, dim=-1, keepdim=True)
-                    u_on_c_mag = (u_dot_c / c_dot_c)
-                    u_on_c = u_on_c_mag * cond
-                    u_rej_c = uncond - u_on_c
-                    rej_dor_rej = torch.sum(u_rej_c * u_rej_c, dim=-1, keepdim=True)
-                    x_final = (cond - squash * u_rej_c + stretch * cond * ((rej_dor_rej/c_dot_c) ** 0.5))
-                    logging.debug(f"NRS.nrs: displaced")
-                case "v0.4.1":
-                    u_dot_c = torch.sum(uncond * cond, dim=-1, keepdim=True)
-                    c_dot_c = torch.sum(cond * cond, dim=-1, keepdim=True)
-                    u_on_c_mag = (u_dot_c / c_dot_c)
-                    u_on_c = u_on_c_mag * cond
-                    u_rej_c = uncond - u_on_c
-                    rej_dor_rej = torch.sum(u_rej_c * u_rej_c, dim=-1, keepdim=True)
-                    stretched = cond + stretch * cond * ((rej_dor_rej/c_dot_c) ** 0.5)
-                    skewed = stretched - skew * u_rej_c
-                    sk_dot_sk = torch.sum(skewed * skewed, dim=-1, keepdim=True)
-                    squash_scale = (1 - squash) + squash * ((c_dot_c/sk_dot_sk) ** 0.5)
-                    x_final = skewed * squash_scale
-                    logging.debug(f"NRS.nrs: displaced")
-                case "v0.4.2":
-                    u_dot_c = torch.sum(uncond * cond, dim=-1, keepdim=True)
-                    c_dot_c = torch.sum(cond * cond, dim=-1, keepdim=True)
-                    u_on_c_mag = (u_dot_c / c_dot_c)
-                    u_on_c = u_on_c_mag * cond
-                    u_rej_c = uncond - u_on_c
-                    proj_len = torch.sum(u_on_c * u_on_c, dim=-1, keepdim=True) ** 0.5
-                    cond_len = c_dot_c ** 0.5
-                    stretched = cond * (1 + stretch * torch.abs(cond_len - proj_len) / cond_len)
-                    skewed = stretched - skew * u_rej_c
-                    sk_dot_sk = torch.sum(skewed * skewed, dim=-1, keepdim=True)
-                    squash_scale = (1 - squash) + squash * cond_len / (sk_dot_sk ** 0.5)
-                    x_final = skewed * squash_scale
-                    logging.debug(f"NRS.nrs: displaced")
-                case "v0.4.3":
-                    u_dot_c = torch.sum(uncond * cond, dim=-1, keepdim=True)
-                    c_dot_c = torch.sum(cond * cond, dim=-1, keepdim=True)
-                    u_on_c_mag = (u_dot_c / c_dot_c)
-                    u_on_c = u_on_c_mag * cond
-                    u_rej_c = uncond - u_on_c
-                    proj_len = torch.sum(u_on_c * u_on_c, dim=-1, keepdim=True) ** 0.5
-                    cond_len = c_dot_c ** 0.5
-                    stretched = cond * (1 + stretch * (cond_len - proj_len) / cond_len)
-                    skewed = stretched - skew * u_rej_c
-                    sk_dot_sk = torch.sum(skewed * skewed, dim=-1, keepdim=True)
-                    squash_scale = (1 - squash) + squash * cond_len / (sk_dot_sk ** 0.5)
-                    x_final = skewed * squash_scale
-                    logging.debug(f"NRS.nrs: displaced")
-                case "v0.4.4":
-                    u_dot_c = torch.sum(uncond * cond, dim=-1, keepdim=True)
-                    c_dot_c = torch.sum(cond * cond, dim=-1, keepdim=True)
-                    u_on_c_mag = (u_dot_c / c_dot_c)
-                    u_on_c = u_on_c_mag * cond
-                    u_rej_c = uncond - u_on_c
-                    cond_len = c_dot_c ** 0.5
-                    proj_diff = cond - u_on_c
-                    proj_diff_len = torch.sum(proj_diff * proj_diff, dim=-1, keepdim=True) ** 0.5
-                    stretched = cond * (1 + stretch * proj_diff_len / cond_len)
-                    skewed = stretched - skew * u_rej_c
-                    sk_dot_sk = torch.sum(skewed * skewed, dim=-1, keepdim=True)
-                    squash_scale = (1 - squash) + squash * cond_len / (sk_dot_sk ** 0.5)
-                    x_final = skewed * squash_scale
-                    logging.debug(f"NRS.nrs: displaced")
-                case "v0.4.5":
-                    u_dot_c = torch.sum(uncond * cond, dim=-1, keepdim=True)
-                    c_dot_c = torch.sum(cond * cond, dim=-1, keepdim=True)
-                    u_on_c_mag = (u_dot_c / c_dot_c)
-                    u_on_c = u_on_c_mag * cond
-                    u_rej_c = uncond - u_on_c
-                    cond_len = c_dot_c ** 0.5
-                    proj_diff = cond - u_on_c
-
-                    # Amplify Cond based on length compared to projection of uncond
-                    stretched = cond + (stretch * proj_diff)
-
-                    # Skew/Steer Conf based on rejection of uncond on cond
-                    skewed = stretched - skew * u_rej_c
-
-                    # Squash final length back down to original length of cond
-                    sk_dot_sk = torch.sum(skewed * skewed, dim=-1, keepdim=True)
-                    squash_scale = (1 - squash) + squash * cond_len / (sk_dot_sk ** 0.5)
-                    x_final = skewed * squash_scale
-
-            return x_orig - (x - x_final * sigma / (sigma * sigma + 1.0) ** 0.5)
+            match self.__OPERATION_SPACE:
+                case PredictionType.V:
+                    return self._finalize_from_v_space(x_orig, x_div, x_final, sig_root, sigma)
+                case PredictionType.EPS:
+                    return self._finalize_from_eps_space(x_orig, x_div, x_final, sig_root, sigma)
+                case PredictionType.X0:
+                    raise RuntimeError("NRS.nrs: x0-prediction not supported yet.")
+                case PredictionType.UNKNOWN:
+                    raise RuntimeError("NRS.nrs: Could not determine prediction type for this operation.")
+                case _:
+                    raise RuntimeError("NRS.nrs: Invalid PredictionType used.")
        
        m = model.clone()
        m.set_model_sampler_cfg_function(nrs, True)
@@ -1,27 +1,79 @@
+[![CodeQL](https://github.com/Reithan/negative_rejection_steering/actions/workflows/github-code-scanning/codeql/badge.svg)](https://github.com/Reithan/negative_rejection_steering/actions/workflows/github-code-scanning/codeql)
+[![ComfyUI Registry](https://github.com/Reithan/negative_rejection_steering/actions/workflows/publish.yml/badge.svg)](https://registry.comfy.org/nodes/negative_rejection_steering)
+
 # Negative Rejection Steering
-NRS seeks to replace the 'naive' linear interpolation of Classifier Free Guidance with a more nuanced steering of the generation process.
+NRS seeks to replace the 'naive' linear interpolation of Classifier Free Guidance with a more nuanced and composable steering of the generation process with better mathematical basis.

-This is accomplised in 3 steps:
-1. **Displacement**: The conditioned output tensor is displaced in the direction of the rejection of the unconditioned tensor on the conditioned tensor. This lengthens the tensor in a direction perpendicular to it's direction without affecting the positive guidance. The tensor is displaced by the rejection x the Displacement parameter.
-2. **Squashing**: The displaced tensor is rescaled towards the original length of the conditioned tensor. This means for high displacement scaling values the tensor 'turns' away from the unconditioned direction, which for very negative displacements, it turns towards the unconditioned tensor. 0 displacement outputs the original conditioned tensor.
-3. **Stretching**: The post-squash 'steered' tensor is stretched towards the direction of the original conditioned tensor. The more sharp the steering the less pronounced the stretch is, with fully aligned tensors being stretched the full stretch scale parameter. 1x stretch adds 100% length to the tensor.
+#### _**TL;DR**_:
+1. CFG is a bad 'knob'
+2. NRS replaces CFG with 3 new knobs.
+3. NRS lets you to create cooler outputs than CFG.

-# Alpha Release
-Implements NRS with Skew, Stretch, and Squash parameters.
+> [!TIP]
+> Skip to the [Beginner How-To](#beginner-how-to) if you want to just get started.

-## Parameters
-Skew and Stretch are roughly similar to CFG, but decomposed, with `Stretch + Skew = CFG`, roughly.
+### Math Demonstration
+<details>
+<summary>Expand for explanation of algorithm</summary>
+<img align="right" src="Examples/NRS_graph.png" alt="Graph of NRS vs CFG" style="width: 40%; float: right;">

-**Skew** changes the 'direction' of generation, which should result in changes to the content and composition of the image.
-**Stretch** changes to 'amplification' of generation, which should result in stronger prompt representation.
-**Squash** 'normalizes' the resulting guidance back towards the original amplitude with 1.0 being the same amplitude, while 0.0 is the unmodified amplitude resulting from the Squash and Stretch functions.
+### NRS is Applied in Three Steps:
+0. ***V-Space**: Optional pre-NRS step* If the model is not using v-prediction, we transform the EPS `cond` and `uncond` into v-prediction space before continuing, then revert to eps-space before return.
+1. **Skewing**: The conditioned output tensor is skewed away from the direction of the rejection of the unconditioned tensor on the conditioned tensor. This lengthens the tensor in a direction perpendicular to its direction without affecting the positive guidance. The tensor is displaced by the rejection multiplied by the Skew parameter.[^1]
+2. **Stretching**: The skewed tensor is stretched towards the direction of the original conditioned tensor based on its difference from the projection of uncond on cond. The stretch is multiplied by the Stretch parameter.[^1]
+3. **Squashing**: The skewed and stretched tensor is rescaled towards the original length of the conditioned tensor. 100% squashing outputs the original length of the conditioned tensor simply 'steered' towards the skewed & squashed version's direction.[^1]
+[^1]: All operations are done per feature across the step's batch, width, and height.
+
+[Interactive Graph on Math3D.org](https://www.math3d.org/aTJW4UZtCh)
+</details>
+
+## Examples of NRS Effects
+**Skew**
+![Skew Example](Examples/skew_array.png)
+**Stretch**
+![Stretch Example](Examples/stretch_array.png)
+**Squash**
+![Squash Example](Examples/squash_matrix.png)
+<details>
+<summary><small>Generation details for reproduction</small></summary>
+
+| Prompt     | |
+| ---------- | --- |
+| Tool       | [Stable Diffusion WebUI reForge](https://github.com/Panchovix/stable-diffusion-webui-reForge) |  
+| Sampler    | DPM++ 2M |
+| Scheduler  | Align Your Steps |
+| Steps      | 25 |
+| Dimensions | 912 x 624 |
+| Seed       | `1334103348` |
+| Model      | [Lobotomized Mix v1.5](https://civitai.com/models/1144932) |
+| Embeddings | [Lazy Embeddings for ALL illustrious NoobAI...](https://civitai.com/models/1302719), [Smooth Embeddings](https://civitai.com/models/1065154) |
+| Positive   | lazypos, [Smooth_Quality\|SmoothNoob_Quality], BREAK<br>very awa, masterpiece, best quality, year 2024, newest, highres, absurdres,<br>1girl, samurai archer, cyberpunk cityscape, rain-soaked rooftop, neon reflection puddles, volumetric mist,<br>photorealistic, digital art,<br>dramatic rim lighting, shallow depth of field, low angle viewpoint |
+| Negative   | lazyloli, lazynsfw, BREAK<br>lazyhand, SmoothNegative_Hands-neg, BREAK<br>[Smooth_Negative-neg\|SmoothNoob_Negative-neg], BREAK<br>lowres, worst quality, worst aesthetic, bad quality, jpeg artifacts, scan artifacts,<br>blurry, deformed anatomy, bad hands, extra fingers, missing fingers, mutated hands,<br>watermark, logo, text, nsfw |
+</details>
+
+### Explanation of Effects
+#### Skew
+**Skew** changes the 'direction' of your generation, altering the image generation to 'steer' away from negative prompt elements as they conflict with your positive prompt. Increasing Skew will change scene composition, geometry, and scene elements to ensure that the final image aligns with the intention of your prompt pair.
+#### Stretch
+**Stretch** changes the intensity of generated elements that align more with your positive prompt than the negative. This 'hits the gas' on any elements that are more strongly aligned with your positive prompt than your negative, and 'hit the brakes' on the opposite.
+#### Squash
+**Squash** is the speed limit. At 0.0 Squash, each diffusion step receives the full intensity you set from Skew and Stretch, while 1.0 Squash ensures each step has only the original step size output by the model. This setting will only remove intensity unless you have a non-zero Skew value. Squash will 'soften' the effects of Skew and Stretch as it's raised, but the 'removed' Skew and Stretch intensity is replaced by enhanced micro-detailing and 'burn'. Squash should generally be left low and used as a 'finishing' step after dialing in a decent Skew and Stretch value.

 ## Beginner How-To
-1. Set Squash to 0.0
-2. Set Skew & Stretch each to 1/2 your normal CFG Scale setting
-3. Test some generation. Results should be 'similar' in quality to CFG
-4. Adjust Skew up/down to change content and composition
-5. Adjust Stretch up/down to change strength of image aspects and colors
-6. Adjust Squash up to remove artifacts and color burn (these will tend to be replaced by additional or extraneous details and elements)
+1. Set Skew to your normal CFG Scale setting and Stretch to 1/2 your normal CFG Scale. Set Squash to 0.0.<br>
+*Alternatively, try starting at 1/1/0.0 to get a baseline.*
+2. Test some outputs. Results should be similar in quality to CFG.
+3. Adjust Skew to change the intensity of your outputs adherence to your positive and negative prompts. This primarily effects composition of the output.
+4. Adjust Stretch to intensify your positive prompt's aspects and colors where they differ from the negative prompt. This primarily effects color and texture.
+5. Adjust Squash to soften Skew and Stretch's effects. The intensity removed from Skew and Stretch will generally become additional micro-detailing and elements.

-**Tip**: You can experiment with negative values for Skew and Stretch as well to see what the model 'believes' your negative prompt 'means'.
+> [!TIP] 
+> You can experiment with negative values for each setting as well. This can be useful to understand how the model interpreting your negative prompt.
+
+> [!WARNING] 
+> Don't set NRS values to negatives if there are things in your negative prompt you **actually** don't want to see.
+
+## Submitted User Examples
+| User | CFG | NRS |
+| --- | --- | --- |
+| Mohnjiles from StabilityMatrix | ![CFG Example](Examples/mohnjiles_cfg.png) | ![NRS Example](Examples/mohnjiles_nrs.png) |
@@ -0,0 +1,5 @@
+from .NRS.nodes_NRS import *
+
+NODE_CLASS_MAPPINGS = {"NRS": NRS}
+NODE_DISPLAY_NAME_MAPPINS = {"NRS": "Negative Rejection Steering"}
+__all__ = ['NODE_CLASS_MAPPINGS', 'NODE_DISPLAY_NAME_MAPPINGS']
@@ -0,0 +1,11 @@
+# The 'purpose' or 'intention' behind our 3 knobs
+
+## SKEW
+This is the primary 'steering' knob. This will 'turn' the 'direction' the current denoising step is traveling in the latent space. If we define the 'default' (cond) direction as 'prior step -> cond' then we're just applying a 'lateral' skew to that direction to 'turn' it 'away' from the 'unintended' direction (uncond)
+
+## STRETCH
+This is the sister knob to Skew. This is the accelerator. We want to go 'faster' into the intended direction (cond) the less aligned it is with the unintended direction (uncond). Think of this like a combination of brakes + gas. If we're headed directly for a brick wall (uncond is in the same direction as cond), we want to apply no acceleration, or negative acceleration. If we're traveling directly away from danger (uncond is in the opposite direction of cond) then we want to stomp the gas and get as far away as we can. There's only 1 problem with this BASIC-level description: as we get further into generation, regardless of pos/neg promp, cond & uncon will naturally align to be the same vector[^1]. In the last stop of inference, cond and uncond will be basically identical if nothing has fucked up. So whatever math we apply here needs to take the progressive alignment of cond & uncond into account. That's why were/are scaling only on the projection difference right now, rather than the full projection.
+[^1]: This is more true in eps than v-pred. Stretch is inherently more powerful in v-pred based models than eps models.
+
+## SQUASH
+This is out 'safety' knob. Think of this like a 'limiter' in a car. This sets the 'top speed' we can go to some multiple of the 'default' speed the model would 'like to' go. i.e. whatever length of directional vector the model produces prior to any skewing or stretching is treated as the 'default' length with Squash=1.0 ensuring we only every go that 'speed' and no more, while Squash=0.0 lets us go any speed we want based on the other 2 knobs. GENERALLY we'll be leaving Squash at 0.0 unless we need it for specific generations.
@@ -0,0 +1,16 @@
+[project]
+name = "negative_rejection_steering"
+description = "NRS seeks to replace the 'naive' linear interpolation of Classifier Free Guidance with a more nuanced and composable steering of the generation process with better mathematical basis."
+authors = [{name = "Bryan O'Malley", email = "bo122081@hotmail.com"}]
+version = "0.7.0"
+license = {file = "LICENSE"}
+readme = "README.md"
+
+[project.urls]
+Repository = "https://github.com/Reithan/negative_rejection_steering"
+#  Used by Comfy Registry https://comfyregistry.org
+
+[tool.comfy]
+PublisherId = "reithan"
+DisplayName = "Negative Rejection Steering"
+Icon = "https://raw.githubusercontent.com/Reithan/negative_rejection_steering/main/icon.png"
@@ -11,28 +11,25 @@ class NRSScript(scripts.Script):
    def __init__(self):
        super().__init__()
        self.enabled = False
-        self.skew = 2.0
+        self.skew = 4.0
        self.stretch = 2.0
-        self.squash = 1.0
+        self.squash = 0.0

    sorting_priority = 5

    def title(self):
-        return "Negative Rejection Steering for reForge"
+        return "Negative Rejection Steering"

    def show(self, is_img2img):
        return scripts.AlwaysVisible
    
    def ui(self, *args, **kwargs):
        with gr.Accordion(open=False, label=self.title()):
-            gr.HTML("<p><i>Adjust the settings for Negative Rejection Steering.</i></p>")
            enabled = gr.Checkbox(label="Enable NRS", value=self.enabled)
-            gr.HTML("<p><i>Adjust the amount guidance is steered.</i></p>")
-            skew = gr.Slider(label="NRS Skew Scale", minimum=-30.0, maximum=30.0, step=0.01, value=self.skew)
-            gr.HTML("<p><i>Adjust the amount guidance is amplified.</i></p>")
-            stretch = gr.Slider(label="NRS Stretch Scale", minimum=-30.0, maximum=30.0, step=0.01, value=self.stretch)
-            gr.HTML("<p><i>Adjust the amount final guidance is normalized.</i></p>")
-            squash = gr.Slider(label="NRS Squash Multiplier", minimum=0.0, maximum=1.0, step=0.01, value=self.squash)
+            gr.HTML("<p><i>Adjust the settings for Negative Rejection Steering.</i></p>")
+            skew = gr.Slider(label="NRS Skew Scale", info="Adjusts the amount guidance is steered.", minimum=-30.0, maximum=30.0, step=0.01, value=self.skew)
+            stretch = gr.Slider(label="NRS Stretch Scale", info="Adjusts the amount guidance is amplified.", minimum=-30.0, maximum=30.0, step=0.01, value=self.stretch)
+            squash = gr.Slider(label="NRS Squash Multiplier", info="Adjusts the amount final guidance is normalized.", minimum=0.0, maximum=1.0, step=0.01, value=self.squash)

        enabled.change(
            lambda x: self.update_enabled(x),
Author	SHA1	Message	Date
Reithan	3d8827f132	Cleanup old math versions and fix variables (#16 )	2025-07-26 04:34:34 -07:00
Reithan	62bef2e275	Update README.md	2025-07-21 04:01:10 -07:00
Reithan	fecdfe01df	Update README.md	2025-07-21 04:00:01 -07:00
Reithan	c5610837e4	Update README.md	2025-07-21 03:59:41 -07:00
Reithan	21ac7cf0cb	Update pyproject.toml (#14 )	2025-07-21 03:56:18 -07:00
Reithan	4930d862d5	Update publish.yml	2025-07-21 03:50:11 -07:00
Robin Huang snomiao Reithan	e8b727f914	Add pyproject.toml for Custom Node Registry (#7 ) Hey! My name is Robin and I'm from [comfy-org](https://comfy.org/)! We would love to have you join the Comfy Registry, a public collection of custom nodes which lets authors publish nodes by version and automate testing against existing workflows. The registry is already integrated with ComfyUI-Manager, and we want it to be the default place users install nodes from eventually. We do a security-scan of every node to improve safety. Feel free to read up more on the registry [here](https://docs.comfy.org/registry/overview#introduction) Action Required: - [ ] Go to the [registry](https://registry.comfy.org). Login and create a publisher id (everything after the `@` sign on your registry profile). - [ ] Add the publisher id into the pyproject.toml file. - [ ] Merge the separate Github Actions PR, then merge this PR. If you want to publish the node manually, [install the cli](https://docs.comfy.org/comfy-cli/getting-started#install-cli) by running `pip install comfy-cli`, then run `comfy node publish` Otherwise, if you have any questions, please message me on discord at robinken or join our [server](https://discord.com/invite/comfyorg)! --------- Co-authored-by: snomiao <snomiao+comfy-pr@gmail.com> Co-authored-by: Reithan <bo122081@hotmail.com>	2025-07-21 03:46:38 -07:00
Reithan	389aedfd17	upload icon	2025-07-21 03:43:44 -07:00
Robin Huang snomiao Reithan	99824b2ee5	Add Github Action for Publishing to Comfy Registry (#6 ) This PR adds a Github Action (publish-node-action) that will publish an updated version of your custom node to the [registry](https://registry.comfy.org/) whenever the `pyproject.toml` file changes. The pyproject.toml defines the custom node version you want to publish (added in another PR). Make sure you update the version number in `pyproject.toml` when you make a change that should be published to everyone! Action Required: - [ ] Make sure the trigger branch (`master` or `main`) in `publish.yaml` matches the branch you want to use as the publishing branch. It will only trigger when the pyproject.toml gets updated on that branch. - [ ] Create an api key on the Registry for publishing from Github. [Instructions](https://docs.comfy.org/registry/publishing#create-an-api-key-for-publishing). - [ ] Add it to your Github Repository Secrets as `REGISTRY_ACCESS_TOKEN`. Please message me on Discord at robinken or join our [server](https://discord.com/invite/comfyorg) server if you have any questions! --------- Co-authored-by: snomiao <snomiao+comfy-pr@gmail.com> Co-authored-by: Reithan <bo122081@hotmail.com>	2025-07-21 01:50:39 -07:00
Reithan	4bb226aabb	Update README.md (#13 )	2025-07-21 01:18:32 -07:00
Reithan	60b5127cf4	Update to Math v0.6.0 (#12 ) - [X] add math 0.6 - [X] update readme - [X] add example images - [X] upload permanent copy of graph image	2025-07-21 01:14:06 -07:00
Reithan	e72afd4189	update note	2025-07-20 07:20:35 -07:00
Reithan	98a9b6d656	minor optimizations	2025-07-20 07:13:07 -07:00
Reithan	e0988d3b24	Fix detection of model type (#11 ) - fix some scaling issues	2025-07-20 04:04:54 -07:00
Reithan	932c7b2136	fix batch size error when applying scale as broadcast	2025-07-19 20:24:28 -07:00
Reithan	50ddd2ac47	Update math to 0.5.0 (#10 ) - [X] detects v-pred/eps and uses appropriate pre/post scaling - [X] supports detection in Forge, Comfy and various loaders/models	2025-07-19 17:59:27 -07:00
Reithan	3768f4a768	Update ComfyUI compatibility (#5 )	2025-04-13 22:13:50 -07:00
Reithan	e82622cefe	Update negative_rejection_steering_script.py	2025-03-29 05:13:16 -07:00
Reithan	793914ced3	Update README.md (#4 ) Correctd steps	2025-03-28 15:52:13 -07:00
Reithan	46540aa7bc	Update README.md Add example images	2025-03-24 00:58:59 -07:00
Reithan	873034b095	add init file for ComfyUI	2025-03-24 00:53:57 -07:00
Reithan	7d399643dd	remove unneeded import	2025-03-24 00:53:44 -07:00
Reithan	50033c2622	add user examples	2025-03-24 00:52:15 -07:00
Reithan	36eb9d592b	Update README.md fix typo	2025-03-22 19:02:55 -07:00
Reithan	bc50983954	Update README.md tl;dr added	2025-03-22 19:01:27 -07:00
Reithan	0c37c6b124	Update README.md Hide math stuff to help overwhelm	2025-03-22 18:56:38 -07:00
Reithan	8d7d9281f7	Update README.md update tip verbiage	2025-03-22 18:51:30 -07:00
Reithan	e55881afe2	Update README.md "it's" to "its"	2025-03-22 18:40:25 -07:00
Reithan	0e8e508213	Update README.md Turn params to bullet points	2025-03-22 18:36:09 -07:00
Reithan	ff69ac386d	Update README.md Move image	2025-03-22 18:32:58 -07:00
Reithan	5be3c4c4f1	Update README.md Update wording and instructions for clarity.	2025-03-22 18:31:19 -07:00
Reithan	81b836d2f1	Update README.md Add Interactive Graph to Readme	2025-03-22 18:18:59 -07:00
Reithan	a57c16a624	Update README.md	2025-03-22 05:06:20 -07:00
Reithan	e6cdf03189	Update module UI style (#2 ) Update module UI style	2025-03-22 01:47:42 -07:00
Reithan	47e4b5073b	Update README.md wording	2025-03-21 22:24:43 -07:00
Reithan	0778fa4b03	Update README.md Update CFG comparison	2025-03-21 22:08:25 -07:00