Upload negative_rejection_steering using SD-Hub

.gitattributes

@@ -185,3 +185,9 @@ sd-webui-chunk-weights/example/1.jpg filter=lfs diff=lfs merge=lfs -text
 sd-webui-chunk-weights/example/2.jpg filter=lfs diff=lfs merge=lfs -text
 sd-webui-chunk-weights/example/3.jpg filter=lfs diff=lfs merge=lfs -text
 sd-webui-chunk-weights/example/4.jpg filter=lfs diff=lfs merge=lfs -text
+negative_rejection_steering/Examples/mohnjiles_cfg.png filter=lfs diff=lfs merge=lfs -text
+negative_rejection_steering/Examples/mohnjiles_nrs.png filter=lfs diff=lfs merge=lfs -text
+negative_rejection_steering/Examples/skew_array.png filter=lfs diff=lfs merge=lfs -text
+negative_rejection_steering/Examples/squash_matrix.png filter=lfs diff=lfs merge=lfs -text
+negative_rejection_steering/Examples/stretch_array.png filter=lfs diff=lfs merge=lfs -text
+negative_rejection_steering/icon.png filter=lfs diff=lfs merge=lfs -text

negative_rejection_steering/.github/workflows/publish.yml

@@ -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 }}

negative_rejection_steering/.gitignore

@@ -0,0 +1,174 @@
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+cover/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+.pybuilder/
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+#   For a library or package, you might want to ignore these files since the code is
+#   intended to run in multiple environments; otherwise, check them in:
+# .python-version
+
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
+#Pipfile.lock
+
+# UV
+#   Similar to Pipfile.lock, it is generally recommended to include uv.lock in version control.
+#   This is especially recommended for binary packages to ensure reproducibility, and is more
+#   commonly ignored for libraries.
+#uv.lock
+
+# poetry
+#   Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
+#   This is especially recommended for binary packages to ensure reproducibility, and is more
+#   commonly ignored for libraries.
+#   https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
+#poetry.lock
+
+# pdm
+#   Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
+#pdm.lock
+#   pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it
+#   in version control.
+#   https://pdm.fming.dev/latest/usage/project/#working-with-version-control
+.pdm.toml
+.pdm-python
+.pdm-build/
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+
+# pytype static type analyzer
+.pytype/
+
+# Cython debug symbols
+cython_debug/
+
+# PyCharm
+#  JetBrains specific template is maintained in a separate JetBrains.gitignore that can
+#  be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
+#  and can be added to the global gitignore or merged into this file.  For a more nuclear
+#  option (not recommended) you can uncomment the following to ignore the entire idea folder.
+#.idea/
+
+# Ruff stuff:
+.ruff_cache/
+
+# PyPI configuration file
+.pypirc

negative_rejection_steering/LICENSE

@@ -0,0 +1,674 @@
+                    GNU GENERAL PUBLIC LICENSE
+                       Version 3, 29 June 2007
+
+ Copyright (C) 2007 Free Software Foundation, Inc. <https://fsf.org/>
+ Everyone is permitted to copy and distribute verbatim copies
+ of this license document, but changing it is not allowed.
+
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negative_rejection_steering/NRS/nodes_NRS.py

@@ -0,0 +1,223 @@
+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,
+    "flux":         PredictionType.EPS,
+    "chroma":       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": 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": 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))
+            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.")
+
+            def _dot(a, b):
+                return (a*b).sum(dim=1, keepdim=True) # [B,C,W,H] => [B,1,W,H]
+
+            def _nrm2(v):
+                return _dot(v, v)
+
+            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)
+
+            # Skew/Steer Conf based on rejection of uncond on cond
+            u_rej_c = nrs_uncond - u_on_c
+            skewed = stretched - (skew * u_rej_c)
+
+            # 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_scale = (1 - squash) + (squash * (cond_len / nrs_len))
+            x_final = skewed * squash_scale
+
+            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)
+        return (m, )
+
+NODE_CLASS_MAPPINGS = {
+    "NRS": NRS,
+}

negative_rejection_steering/README.md

@@ -0,0 +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 and composable steering of the generation process with better mathematical basis.
+
+#### _**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.
+
+> [!TIP]
+> Skip to the [Beginner How-To](#beginner-how-to) if you want to just get started.
+
+### 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;">
+
+### 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 Skew to 1/2 of your normal CFG Scale setting and Stretch to your full normal CFG Scale. Set Squash to 0.0.<br>
+*Alternatively, try starting at 1/1/1 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 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) |

negative_rejection_steering/__init__.py

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+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']

negative_rejection_steering/note.md

@@ -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.

negative_rejection_steering/pyproject.toml

@@ -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 = "0.7.2"
+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"

negative_rejection_steering/scripts/negative_rejection_steering_script.py

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+import torch
+import gradio as gr
+from modules import scripts, script_callbacks, sd_samplers_cfg_denoiser, shared
+
+# ==============================================================================
+# ЧАСТЬ 1: Математическое ядро NRS
+# ==============================================================================
+def calc_nrs(x_orig, cond, uncond, sigma, skew, stretch, squash):
+    # 1. Проверка режима V-prediction
+    is_v_pred = False
+    if hasattr(shared.sd_model, 'parameterization'):
+        is_v_pred = shared.sd_model.parameterization == "v"
+    
+    # 2. Подготовка тензора Sigma
+    if isinstance(sigma, torch.Tensor):
+        sig_tens = sigma[0] 
+    else:
+        sig_tens = torch.tensor(sigma, device=cond.device, dtype=cond.dtype)
+    
+    if sig_tens.dtype != cond.dtype:
+        sig_tens = sig_tens.to(dtype=cond.dtype)
+
+    sig_tens = sig_tens.view(1, 1, 1, 1)
+    sig_root = (sig_tens ** 2 + 1).sqrt()
+
+    # 3. Конвертация EPS -> V
+    if is_v_pred:
+        nrs_cond, nrs_uncond = cond, uncond
+        x_div = None
+    else:
+        x_div = x_orig / (sig_tens ** 2 + 1)
+        factor = sig_tens / sig_root
+        nrs_cond = x_orig - (x_div - cond * factor)
+        nrs_uncond = x_orig - (x_div - uncond * factor)
+
+    # 4. Векторные операции (Core NRS Math)
+    def _dot(a, b): return (a * b).sum(dim=1, keepdim=True)
+    def _nrm2(v): return _dot(v, v)
+
+    eps_safe = 1e-6
+    
+    # Проекция Uncond на Cond
+    c_dot_c = _nrm2(nrs_cond) + eps_safe
+    u_dot_c = _dot(nrs_uncond, nrs_cond)
+    u_on_c = (u_dot_c / c_dot_c) * nrs_cond
+    
+    # Stretch: Усиление
+    proj_diff = nrs_cond - u_on_c
+    stretched = nrs_cond + (stretch * proj_diff)
+
+    # Skew: Отклонение
+    u_rej_c = nrs_uncond - u_on_c
+    skewed = stretched - (skew * u_rej_c)
+
+    # Squash: Нормализация
+    cond_len = nrs_cond.norm(dim=1, keepdim=True)
+    nrs_len = skewed.norm(dim=1, keepdim=True) + eps_safe
+    
+    squash_scale = (1 - squash) + (squash * (cond_len / nrs_len))
+    x_final = skewed * squash_scale
+
+    # 5. Возврат в исходное пространство
+    if is_v_pred:
+        return x_final
+    else:
+        return (x_div - (x_orig - x_final)) * (sig_root / sig_tens)
+
+
+# ==============================================================================
+# ЧАСТЬ 2: Перехват управления (Hooking)
+# ==============================================================================
+
+# Хук 1: Сохраняем Sigma и Input Latent
+def hook_cfg_denoiser_params(params):
+    if hasattr(params.denoiser, 'p') and getattr(params.denoiser.p, '_nrs_enabled', False):
+        params.denoiser.p._nrs_current_sigma = params.sigma
+        params.denoiser.p._nrs_current_x_in = params.x
+
+script_callbacks.on_cfg_denoiser(hook_cfg_denoiser_params)
+
+# Бэкап оригинала
+if not hasattr(sd_samplers_cfg_denoiser.CFGDenoiser, 'original_combine_denoised_nrs_backup'):
+    sd_samplers_cfg_denoiser.CFGDenoiser.original_combine_denoised_nrs_backup = sd_samplers_cfg_denoiser.CFGDenoiser.combine_denoised
+
+# Хук 2: Подменная функция
+def hijacked_combine_denoised(self, x_out, conds_list, uncond, cond_scale):
+    # Проверка активности NRS
+    if not getattr(self, 'p', None) or not getattr(self.p, '_nrs_enabled', False):
+        return sd_samplers_cfg_denoiser.CFGDenoiser.original_combine_denoised_nrs_backup(self, x_out, conds_list, uncond, cond_scale)
+
+    if not hasattr(self.p, '_nrs_current_sigma') or not hasattr(self.p, '_nrs_current_x_in'):
+        return sd_samplers_cfg_denoiser.CFGDenoiser.original_combine_denoised_nrs_backup(self, x_out, conds_list, uncond, cond_scale)
+
+    try:
+        skew, stretch, squash = self.p._nrs_params
+        
+        denoised_uncond = x_out[-uncond.shape[0]:]
+        denoised = torch.clone(denoised_uncond)
+        
+        x_orig_all = self.p._nrs_current_x_in
+        x_orig_uncond = x_orig_all[-uncond.shape[0]:]
+
+        for i, conds in enumerate(conds_list):
+            for idx, (cond_index, weight) in enumerate(conds):
+                current_cond = x_out[cond_index]
+                
+                # NRS только для основного промпта
+                if idx == 0:
+                    current_x_orig = x_orig_uncond[i].unsqueeze(0)
+                    c_in = current_cond.unsqueeze(0)
+                    u_in = denoised_uncond[i].unsqueeze(0)
+                    
+                    nrs_result = calc_nrs(
+                        current_x_orig, c_in, u_in, 
+                        self.p._nrs_current_sigma, 
+                        skew, stretch, squash
+                    )
+                    
+                    if len(conds) == 1:
+                        denoised[i] = nrs_result.squeeze(0)
+                    else:
+                        delta = nrs_result.squeeze(0) - denoised_uncond[i]
+                        denoised[i] += delta * weight
+                else:
+                    denoised[i] += (current_cond - denoised_uncond[i]) * (weight * cond_scale)
+
+        return denoised
+
+    except Exception as e:
+        print(f"!!! NRS Error (Fallback): {e}")
+        return sd_samplers_cfg_denoiser.CFGDenoiser.original_combine_denoised_nrs_backup(self, x_out, conds_list, uncond, cond_scale)
+
+
+# ==============================================================================
+# ЧАСТЬ 3: Интерфейс (UI) с пояснениями
+# ==============================================================================
+class NRSScript(scripts.Script):
+    def title(self):
+        return "Negative Rejection Steering"
+
+    def show(self, is_img2img):
+        return scripts.AlwaysVisible
+
+    def ui(self, is_img2img):
+        with gr.Accordion("Negative Rejection Steering", open=False):
+            with gr.Row():
+                enabled = gr.Checkbox(label="Включить NRS (Enable)", value=False)
+            
+            # --- БЛОК СПРАВКИ ---
+            with gr.Accordion("❓ Как этим пользоваться (Инструкция)", open=False):
+                gr.Markdown("""
+                ### Что это такое?
+                **NRS** — это замена стандартному CFG Scale. Вместо простого усиления промпта, он дает вам 3 рычага управления для более точного контроля.
+                
+                ### Параметры:
+                * **Skew (Сдвиг/Руление):** *Влияет на:* **Композицию и Геометрию**.  
+                    Отклоняет генерацию *прочь* от того, что указано в Negative prompt. Если картинка "ломается" или лезут лишние объекты — крутите это.
+                * **Stretch (Растяжение):** *Влияет на:* **Цвета и Текстуры**.  
+                    Усиливает элементы, которые совпадают с вашим Positive prompt. Работает как "газ" для ваших идей.
+                * **Squash (Сплющивание):** *Влияет на:* **Детализацию и Контраст**.  
+                    Ограничитель скорости. При `0.0` эффект максимальный (может "пережарить" картинку). Увеличение добавляет микро-деталей и смягчает цвета.
+                
+                ### 🚀 С чего начать (Советы от автора):
+                1.  **Skew** ≈ Половина вашего обычного CFG (например, **3.0** - **4.0**).
+                2.  **Stretch** ≈ Ваш обычный CFG (например, **6.0** - **7.0**).
+                3.  **Squash** ≈ Оставьте **0.0** для начала.
+                """)
+
+            # --- ЭЛЕМЕНТЫ УПРАВЛЕНИЯ ---
+            gr.HTML("<div style='margin-bottom: 0.5em; opacity: 0.8; font-size: 0.9em; border-bottom: 1px solid #444;'>Настройки</div>")
+            
+            with gr.Row():
+                skew = gr.Slider(
+                    label="Skew (Композиция)", 
+                    minimum=-30.0, maximum=30.0, step=0.05, value=4.0, 
+                    info="Сила отклонения от Negative prompt. Аналог 'силы' CFG для структуры."
+                )
+                stretch = gr.Slider(
+                    label="Stretch (Цвета/Текстура)", 
+                    minimum=-30.0, maximum=30.0, step=0.05, value=2.0, 
+                    info="Сила притяжения к Positive prompt. Усиливает цвета и стиль."
+                )
+            
+            squash = gr.Slider(
+                label="Squash (Защита от пережарки)", 
+                minimum=0.0, maximum=1.0, step=0.01, value=0.0, 
+                info="0.0 = Макс. эффект. 1.0 = Ослабление (больше деталей, меньше контраста)."
+            )
+            
+        return [enabled, skew, stretch, squash]
+
+    def process(self, p, enabled, skew, stretch, squash):
+        p._nrs_enabled = enabled
+        p._nrs_params = (skew, stretch, squash)
+        
+        if enabled:
+            sd_samplers_cfg_denoiser.CFGDenoiser.combine_denoised = hijacked_combine_denoised
+
+    def postprocess(self, p, processed, *args):
+        pass