Fix detection of model type (#11)

- fix some scaling issues

NRS/nodes_NRS.py

@@ -1,45 +1,145 @@
-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 _pre_scale_conditioning(self, x_orig, 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._pre_scale_conditioning: generating x_div, cond, and uncond for v-pred")
+            sigma2_1 = (sigma ** 2 + 1.0)
+            x_div = x_orig / sigma2_1
+            root = sigma2_1.sqrt()
+
+            eps_cond = ((x_div - (x_orig - cond)) * root) / (sigma)
+            eps_uncond = ((x_div - (x_orig - uncond)) * root) / (sigma)
+        elif self.__pred_type == PredictionType.EPS:
+            logging.debug(f"NRS._pre_scale_conditioning: already in eps, no pre-scale needed")
+            pass  # already in ε space
+        elif self.__pred_type == PredictionType.X0:
+            raise NotImplementedError("NRS._pre_scale_conditioning: x0-prediction not supported yet.")
+        else:
+            raise RuntimeError("NRS._pre_scale_conditioning: Could not determine prediction type for this model.")
+        
+        return x_div, eps_cond, eps_uncond
+
+    def _post_scale_conditioning(self, x_orig, x_div, x_final, sigma):
+        if self.__pred_type == PredictionType.V:
+            # ε → v conversion
+            root = (sigma ** 2 + 1).sqrt()
+
+            logging.debug(f"NRS._post_scale_conditioning: generating cfg_result for v-pred")
+            return x_orig - (x_div - x_final * sigma / root)
+        elif self.__pred_type == PredictionType.EPS:
+            # already in ε space
+            logging.debug(f"NRS._post_scale_conditioning: already in eps, no post-scale needed")
+            return x_final
+        elif self.__pred_type == PredictionType.X0:
+            raise NotImplementedError("NRS._post_scale_conditioning: x0-prediction not supported yet.")
+        else:
+            raise RuntimeError("NRS._post_scale_conditioning: Could not determine prediction type for this model.")
+        
     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
+
         def nrs(args):
             cond = args["cond"]
             uncond = args["uncond"]
-            sigma = args["sigma"]
-            sigma = sigma.view(sigma.shape[:1] + (1,) * (cond.ndim - 1))
             x_orig = args["input"]
+            
+            self.__pred_type = self.__pred_type if self.__pred_type is not None else self._get_pred_type(model)
+            sigma = None
+            if self.__pred_type == PredictionType.V:
+                sigma = args["sigma"]
+                sigma = sigma.view(sigma.shape[:1] + (1,) * (cond.ndim - 1))
+
 
             logging.debug(f"NRS.nrs: Skew: {skew}, Stretch: {stretch}, Squash: {squash}")
-
-            #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")
+            
+            x_div, eps_cond, eps_uncond = self._pre_scale_conditioning(x_orig, sigma, cond, uncond)
 
             x_final = None
-            match "v0.4.5":
+            match "v0.5.0":
                 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)
+                    u_dot_c = torch.sum(eps_uncond * eps_cond, dim=-1, keepdim=True)
+                    c_dot_c = torch.sum(eps_cond * eps_cond, dim=-1, keepdim=True)
+                    u_on_c = (u_dot_c / c_dot_c) * eps_cond
+                    u_rej_c = eps_uncond - u_on_c
+                    displaced = (eps_cond - skew * u_rej_c)
                     logging.debug(f"NRS.nrs: displaced")
 
                     # squash displaced vector towards len(cond) based on squash scale
@@ -49,18 +149,18 @@ class NRS:
                     logging.debug(f"NRS.nrs: squashed")
 
                     # 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
+                    sq_dot_c = torch.sum(squashed * eps_cond, dim=-1, keepdim=True)
+                    sq_on_c = (sq_dot_c / c_dot_c) * eps_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_dot_c = torch.sum(eps_uncond * eps_cond, dim=-1, keepdim=True)
+                    c_dot_c = torch.sum(eps_cond * eps_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
+                    u_on_c = u_on_c_mag * eps_cond
+                    u_rej_c = eps_uncond - u_on_c
+                    displaced = eps_cond + stretch * (eps_cond - torch.clamp(u_dot_c / c_dot_c, min=0, max=1) * eps_cond) - skew * u_rej_c
                     logging.debug(f"NRS.nrs: displaced & stretched")
 
                     # squash displaced vector towards len(cond) based on squash scale
@@ -70,12 +170,12 @@ class NRS:
                     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_dot_c = torch.sum(eps_uncond * eps_cond, dim=-1, keepdim=True)
+                    c_dot_c = torch.sum(eps_cond * eps_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)
+                    u_on_c = u_on_c_mag * eps_cond
+                    u_rej_c = eps_uncond - u_on_c
+                    displaced = (eps_cond - skew * u_rej_c)
                     logging.debug(f"NRS.nrs: displaced")
 
                     # squash displaced vector towards len(cond) based on squash scale
@@ -89,81 +189,81 @@ class NRS:
                     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_dot_c = torch.sum(eps_uncond * eps_cond, dim=-1, keepdim=True)
+                    c_dot_c = torch.sum(eps_cond * eps_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
+                    u_on_c = u_on_c_mag * eps_cond
+                    u_rej_c = eps_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))
+                    x_final = (eps_cond - squash * u_rej_c + stretch * eps_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_dot_c = torch.sum(eps_uncond * eps_cond, dim=-1, keepdim=True)
+                    c_dot_c = torch.sum(eps_cond * eps_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
+                    u_on_c = u_on_c_mag * eps_cond
+                    u_rej_c = eps_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)
+                    stretched = eps_cond + stretch * eps_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_dot_c = torch.sum(eps_uncond * eps_cond, dim=-1, keepdim=True)
+                    c_dot_c = torch.sum(eps_cond * eps_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
+                    u_on_c = u_on_c_mag * eps_cond
+                    u_rej_c = eps_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)
+                    stretched = eps_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_dot_c = torch.sum(eps_uncond * eps_cond, dim=-1, keepdim=True)
+                    c_dot_c = torch.sum(eps_cond * eps_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
+                    u_on_c = u_on_c_mag * eps_cond
+                    u_rej_c = eps_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)
+                    stretched = eps_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_dot_c = torch.sum(eps_uncond * eps_cond, dim=-1, keepdim=True)
+                    c_dot_c = torch.sum(eps_cond * eps_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
+                    u_on_c = u_on_c_mag * eps_cond
+                    u_rej_c = eps_uncond - u_on_c
                     cond_len = c_dot_c ** 0.5
-                    proj_diff = cond - u_on_c
+                    proj_diff = eps_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)
+                    stretched = eps_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_dot_c = torch.sum(eps_uncond * eps_cond, dim=-1, keepdim=True)
+                    c_dot_c = torch.sum(eps_cond * eps_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
+                    u_on_c = u_on_c_mag * eps_cond
+                    u_rej_c = eps_uncond - u_on_c
                     cond_len = c_dot_c ** 0.5
-                    proj_diff = cond - u_on_c
+                    proj_diff = eps_cond - u_on_c
 
                     # Amplify Cond based on length compared to projection of uncond
-                    stretched = cond + (stretch * proj_diff)
+                    stretched = eps_cond + (stretch * proj_diff)
 
                     # Skew/Steer Conf based on rejection of uncond on cond
                     skewed = stretched - skew * u_rej_c
@@ -172,8 +272,34 @@ class NRS:
                     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
+                case "v0.5.0":
+                    def _dot(a, b):
+                        return (a*b).flatten(1).sum(dim=1, keepdim=True) # [B,C,W,H] => [B,1]
+                    def _nrm2(v):
+                        return _dot(v, v)
 
-            return x_orig - (x - x_final * sigma / (sigma * sigma + 1.0) ** 0.5)
+                    eps = torch.finfo(eps_cond.dtype).eps
+                    c_dot_c = _nrm2(eps_cond) + eps # [B,1]
+                    u_dot_c = _dot(eps_uncond, eps_cond) # [B,1]
+
+                    u_on_c = (u_dot_c / c_dot_c).view(-1, 1, 1, 1) * eps_cond # [B,1,1,1] * [B,C,H,W]
+                    
+                    # Amplify Cond based on length compared to projection of uncond
+                    proj_diff = eps_cond - u_on_c
+                    stretched = eps_cond + (stretch * proj_diff)
+
+                    # Skew/Steer Conf based on rejection of uncond on cond
+                    u_rej_c = eps_uncond - u_on_c
+                    skewed = stretched - (skew * u_rej_c)
+
+                    # Squash final length back down to original length of cond
+                    cond_len = torch.sqrt(c_dot_c) # [B,1]
+                    nrs_len = torch.sqrt(_nrm2(skewed)) + eps # [B,1]
+
+                    squash_scale = (1 - squash) + (squash * (cond_len / nrs_len))
+                    x_final = skewed * squash_scale.view(-1, 1, 1, 1)
+
+            return self._post_scale_conditioning(x_orig, x_div, x_final, sigma)
         
         m = model.clone()
         m.set_model_sampler_cfg_function(nrs, True)

README.md

@@ -1,27 +1,44 @@
 # Negative Rejection Steering
 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.
+### Math Demonstration
+<details>
+<summary>Expand for explanation of algorithm</summary>
+<img align="right" src="https://github.com/user-attachments/assets/01fabaff-8499-45f6-adad-d54b2c2fb7f1" alt="Graph of NRS vs CFG" style="width: 40%; float: right;">
+
+### NRS is Applied in Three Steps:
+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.
+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.
+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.
+
+[Interactive Graph on Math3D.org](https://www.math3d.org/aTJW4UZtCh)
+</details>
 
 ## Parameters
-Skew and Stretch are roughly similar to CFG, but decomposed, with `Stretch + Skew = 2 * CFG`, roughly.
+Skew and Stretch are roughly similar to CFG, but decomposed, with `Stretch + 2 * Skew = 2 * CFG`, roughly.
+Meaning, if you want to 'replicate' a simliar effect for a given CFG setting, you should set Skew equal to CFG, and Stretch to 1/2 CFG.
+Squash should initially be set to 0%, then adjusted based on 'burn' of output.
 
-**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.
+- **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. This results in a removal of 'burn-in' and artifacting of the output, transforming these defects into alternative guidance.
 
 ## Beginner How-To
-1. Set Squash to 0.0
-2. Set Skew & Stretch each to 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.
+2. Set Squash to 0.0.
+3. Test some outputs. 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 positive prompt aspects and colors.
+6. Adjust Squash up to remove artifacts and color burn (these will tend to be replaced by additional details 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 Skew and Stretch as well, to see how the model is interpeting your negative prompt.
+
+## Examples
+| User | CFG | NRS |
+|---|---|---|
+| Mohnjiles from StabilityMatrix | ![CFG Example](Examples/mohnjiles_cfg.png) | ![NRS Example](Examples/mohnjiles_nrs.png) |

__init__.py

@@ -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']

note.md

@@ -0,0 +1,10 @@
+# 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. 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.
+
+## 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.

scripts/negative_rejection_steering_script.py

@@ -11,14 +11,14 @@ 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