v0.4.0

Update Github Action for Publishing to Comfy Registry

.github/workflows/publish.yml

@@ -0,0 +1,25 @@
+name: Publish to Comfy registry
+on:
+  workflow_dispatch:
+  push:
+    branches:
+      - master
+    paths:
+      - "pyproject.toml"
+
+permissions:
+  issues: write
+
+jobs:
+  publish-node:
+    name: Publish Custom Node to registry
+    runs-on: ubuntu-latest
+    if: ${{ github.repository_owner == 'asagi4' }}
+    steps:
+      - name: Check out code
+        uses: actions/checkout@v4
+      - name: Publish Custom Node
+        uses: Comfy-Org/publish-node-action@v1
+        with:
+          ## Add your own personal access token to your Github Repository secrets and reference it here.
+          personal_access_token: ${{ secrets.REGISTRY_ACCESS_TOKEN }}

README.md

@@ -4,6 +4,8 @@ An implementation of adaptive guidance for ComfyUI
 
 See https://bcv-uniandes.github.io/adaptiveguidance-wp/
 
+Import [this workflow](example_workflows/AGExample.json?raw=1) into ComfyUI to compare Adaptive Guidance vs. normal CFG.
+
 ## What
 
 There's an `AdaptiveGuidance` node (under `sampling/custom_sampling/guiders`) that can be used with `SamplerCustomAdvanced`. Normally, you should keep the threshold quite high, between `0.99` and `1.0`
@@ -11,3 +13,11 @@ There's an `AdaptiveGuidance` node (under `sampling/custom_sampling/guiders`) th
 The node calculates the cosine similarity between the u-net's conditional and unconditional output ("positive" and "negative" prompts) and once the similarity crosses the specified threshold, it sets CFG to 1.0, effectively skipping negative prompt calculations and speeding up inference.
 
 I'm not sure if the cosine similarity calculation matches the original paper since I had to translate from maths to Python, but it appears to work.
+
+### Uncond zero
+
+Set uncond_zero_scale to > 0 to enable "uncond zero" CFG *after* the normal CFG gets disabled. Stolen from https://github.com/Extraltodeus/Uncond-Zero-for-ComfyUI
+
+It seems to work slightly better than just running without CFG, but YMMV
+
+Note: this functionality is unstable and will probably change, so using it means your workflows likely won't be perfectly reproducible.

__init__.py

@@ -1,170 +1,69 @@
 import comfy.samplers
+import comfy_extras.nodes_perpneg
 import torch
 
 cos = torch.nn.CosineSimilarity(dim=1)
 
 
-class AdaptiveGuider(comfy.samplers.CFGGuider):
+# shared structure for adaptive guiders
+class AdaptiveGuider(object):
+    cfg_start_timestep = 1000.0
     threshold_timestep = 0
+    uz_scale = 0.0
 
-    def set_threshold(self, threshold):
+    def set_threshold(self, threshold, start_at):
+        self.cfg_start_timestep = start_at
         self.threshold = threshold
 
-    def check_cos_sim(self, ts, cond_pred, uncond_pred):
-        # Is this reshape correct? It at least gives a scalar value...
-        sim = cos(cond_pred.reshape(1, -1), uncond_pred.reshape(1, -1)).item()
-        sim = round(sim, 4)
-        if sim > self.threshold:
-            print("AdaptiveGuidance: Cosine similarity", sim, "exceeds threshold, setting CFG to 1.0")
-            self.threshold_timestep = ts
+    def set_uncond_zero_scale(self, scale):
+        self.uz_scale = scale
+
+    def zero_cond(self, args):
+        cond = args["cond_denoised"]
+        x = args["input"]
+        x -= x.mean()
+        cond -= cond.mean()
+        return x - (cond / cond.std() ** 0.5) * self.uz_scale
+
+    def check_similarity(self, ts, cond_pred, uncond_pred):
+        if not self.threshold >= 1.0:
+            sim = cos(cond_pred.reshape(1, -1), uncond_pred.reshape(1, -1)).item()
+            if sim >= self.threshold:
+                print(f"AdaptiveGuider: Cosine similarity {sim:.4f} exceeds threshold, setting CFG to 1.0")
+                self.threshold_timestep = ts
 
     def predict_noise(self, x, timestep, model_options={}, seed=None):
-        cond = self.conds.get("positive")
-        uncond = self.conds.get("negative")
         ts = timestep[0].item()
-        if self.threshold_timestep > ts:
+        if ts > self.cfg_start_timestep or self.threshold_timestep > ts or self.cfg == 1.0:
+            if self.uz_scale > 0.0:
+                model_options = model_options.copy()
+                model_options["sampler_cfg_function"] = self.zero_cond
+            cond = self.conds.get("positive")
+            uncond = self.conds.get("negative")
             return comfy.samplers.sampling_function(
                 self.inner_model, x, timestep, uncond, cond, 1.0, model_options=model_options, seed=seed
             )
-        else:
-            self.threshold_timestep = 0
-            uncond_pred, cond_pred = comfy.samplers.calc_cond_batch(
-                self.inner_model, [uncond, cond], x, timestep, model_options
-            )
-            self.check_cos_sim()
-            return comfy.samplers.cfg_function(
-                self.inner_model,
-                cond_pred,
-                uncond_pred,
-                self.cfg,
-                x,
-                timestep,
-                model_options=model_options,
-                cond=cond,
-                uncond=uncond,
-            )
+        self.threshold_timestep = 0
+        conds = self.calc_conds(x, timestep, model_options)
+        self.check_similarity(ts, conds[0], conds[1])
+        return self.calc_cfg(conds, x, timestep, model_options)
 
 
-class AdaptiveGuidance:
-    @classmethod
-    def INPUT_TYPES(s):
-        return {
-            "required": {
-                "model": ("MODEL",),
-                "positive": ("CONDITIONING",),
-                "negative": ("CONDITIONING",),
-                "threshold": ("FLOAT", {"default": 0.990, "min": 0.90, "max": 1.0, "step": 0.001, "round": 0.001}),
-                "cfg": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0, "step": 0.1, "round": 0.01}),
-            }
-        }
-
-    RETURN_TYPES = ("GUIDER",)
-    FUNCTION = "patch"
-
-    CATEGORY = "sampling/custom_sampling/guiders"
-
-    def patch(self, model, positive, negative, threshold, cfg):
-        g = AdaptiveGuider(model)
-        g.set_conds(positive, negative)
-        g.set_cfg(cfg)
-        g.set_threshold(threshold)
-
-        return (g,)
-
-
-class LinearAdaptiveGuidance:
-    @classmethod
-    def INPUT_TYPES(s):
-        return {
-            "required": {
-                "model": ("MODEL",),
-                "positive": ("CONDITIONING",),
-                "negative": ("CONDITIONING",),
-                "cfg": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0, "step": 0.1, "round": 0.01}),
-                "threshold": ("FLOAT", {"default": 0.990, "min": 0.90, "max": 1.0, "step": 0.001, "round": 0.001}),
-                "betas_cond": ("STRING", {"default": "0.4,0.2,0.05"}),
-                "betas_uncond": ("STRING", {"default": "0.4,0.2,0.05"}),
-            }
-        }
-
-    RETURN_TYPES = ("GUIDER",)
-    FUNCTION = "patch"
-
-    CATEGORY = "sampling/custom_sampling/guiders"
-
-    def patch(self, model, positive, negative, cfg, threshold, betas_cond, betas_uncond):
-        g = LinearAdaptiveGuider(model)
-        g.set_conds(positive, negative)
-        g.set_cfg(cfg)
-        g.set_threshold(threshold)
-
-        def split_floats(string):
-            return [float(x.strip()) for x in string.split(",")]
-
-        g.set_betas(split_floats(betas_cond), split_floats(betas_uncond))
-
-        return (g,)
-
-
-class LinearAdaptiveGuider(AdaptiveGuider):
-    last_seen_sigma = 0
-
-    def set_betas(self, beta_cond, beta_uncond):
-        self.beta_cond = beta_cond
-        self.beta_uncond = beta_uncond
-
-    def get_beta(self, beta_list):
-        idx = min(self.counter - 1, len(beta_list) - 1)
-        return beta_list[idx]
-
-    def initialize(self):
-        self.cond_results = []
-        self.uncond_results = []
-        self.counter = 0
-
-    def predict_linear(self):
-        return torch.stack(self.cond_results, dim=0).sum(dim=0) + torch.stack(self.uncond_results, dim=0).sum(dim=0)
-
-    def predict_noise(self, x, timestep, model_options={}, seed=None):
+class Guider_AdaptiveGuidance(AdaptiveGuider, comfy.samplers.CFGGuider):
+    def calc_conds(self, x, timestep, model_options):
         cond = self.conds.get("positive")
         uncond = self.conds.get("negative")
-        ts = timestep[0].item()
-        # Not exactly correct, but will work
-        if self.last_seen_sigma < ts:
-            self.initialize()
-        self.last_seen_sigma = ts
-        self.counter += 1
-        if ts < self.threshold_timestep:
-            return comfy.samplers.sampling_function(
-                self.inner_model, x, timestep, uncond, cond, 1.0, model_options=model_options, seed=seed
-            )
+        return comfy.samplers.calc_cond_batch(self.inner_model, [cond, uncond], x, timestep, model_options)
 
-        else:
-            self.threshold_timestep = 0
-        bc = self.get_beta(self.beta_cond)
-        buc = self.get_beta(self.beta_uncond)
-        print(f"LinearAdaptive: {bc=} {buc=}")
-        if self.counter % 2 != 0:
-            # cfg step
-            print("LinearAdaptive: Full CFG step")
-            uncond_pred, cond_pred = comfy.samplers.calc_cond_batch(
-                self.inner_model, [uncond, cond], x, timestep, model_options
-            )
-            self.cond_results.append(cond_pred * bc)
-            self.uncond_results.append(uncond_pred * buc)
-        else:
-            # non-cfg step
-            print("LinearAdaptive: Estimated CFG step")
-            cond_pred = comfy.samplers.calc_cond_batch(self.inner_model, [cond], x, timestep, model_options)[0]
-            self.cond_results.append(cond_pred * bc)
-            uncond_pred = self.predict_linear()
-            self.uncond_results.append(uncond_pred * buc)
+    def calc_cfg(self, conds, x, timestep, model_options):
+        cond = self.conds.get("positive")
+        uncond = self.conds.get("negative")
+        cond_pred, uncond_pred = conds
 
-        self.check_cos_sim(ts, cond_pred, uncond_pred)
         return comfy.samplers.cfg_function(
             self.inner_model,
-            uncond_pred,
             cond_pred,
+            uncond_pred,
             self.cfg,
             x,
             timestep,
@@ -174,7 +73,227 @@ class LinearAdaptiveGuider(AdaptiveGuider):
         )
 
 
+class Guider_PerpNegAG(AdaptiveGuider, comfy_extras.nodes_perpneg.Guider_PerpNeg):
+    def calc_conds(self, x, timestep, model_options):
+        cond = self.conds.get("positive")
+        uncond = self.conds.get("negative")
+        empty_cond = self.conds.get("empty_negative_prompt")
+        return comfy.samplers.calc_cond_batch(self.inner_model, [cond, uncond, empty_cond], x, timestep, model_options)
+
+    def calc_cfg(self, conds, x, timestep, model_options):
+        cond = self.conds.get("positive")
+        uncond = self.conds.get("negative")
+        empty_cond = self.conds.get("empty_negative_prompt")
+        cond_pred, uncond_pred, empty_cond_pred = conds
+        cfg_result = comfy_extras.nodes_perpneg.perp_neg(
+            x, cond_pred, uncond_pred, empty_cond_pred, self.neg_scale, self.cfg
+        )
+        for fn in model_options.get("sampler_post_cfg_function", []):
+            args = {
+                "denoised": cfg_result,
+                "cond": cond,
+                "uncond": uncond,
+                "model": self.inner_model,
+                "uncond_denoised": uncond_pred,
+                "cond_denoised": cond_pred,
+                "sigma": timestep,
+                "model_options": model_options,
+                "input": x,
+                # not in the original call in samplers.py:cfg_function, but made available for future hooks
+                "empty_cond": empty_cond,
+                "empty_cond_denoised": empty_cond_pred,
+            }
+            cfg_result = fn(args)
+
+        return cfg_result
+
+
+class AdaptiveGuidanceGuider:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "model": ("MODEL",),
+                "positive": ("CONDITIONING",),
+                "negative": ("CONDITIONING",),
+                "threshold": ("FLOAT", {"default": 0.990, "min": 0.90, "max": 1.0, "step": 0.0001, "round": 0.0001}),
+                "cfg": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0, "step": 0.1, "round": 0.01}),
+            },
+            "optional": {
+                "uncond_zero_scale": ("FLOAT", {"default": 0.0, "max": 2.0, "step": 0.01}),
+                "cfg_start_pct": ("FLOAT", {"default": 0.0, "max": 1.0, "step": 0.01}),
+            },
+        }
+
+    RETURN_TYPES = ("GUIDER",)
+    FUNCTION = "get_guider"
+
+    CATEGORY = "sampling/custom_sampling/guiders"
+
+    def get_guider(self, model, positive, negative, threshold, cfg, uncond_zero_scale=0.0, cfg_start_pct=0.0):
+        cfg_start_timestep = model.get_model_object("model_sampling").percent_to_sigma(cfg_start_pct)
+        g = Guider_AdaptiveGuidance(model)
+        g.set_conds(positive, negative)
+        g.set_threshold(threshold, cfg_start_timestep)
+        g.set_uncond_zero_scale(uncond_zero_scale)
+        g.set_cfg(cfg)
+
+        return (g,)
+
+
+class PerpNegAGGuider:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "model": ("MODEL",),
+                "positive": ("CONDITIONING",),
+                "negative": ("CONDITIONING",),
+                "empty_conditioning": ("CONDITIONING",),
+                "threshold": ("FLOAT", {"default": 0.990, "min": 0.90, "max": 1.0, "step": 0.0001, "round": 0.0001}),
+                "cfg": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0, "step": 0.1, "round": 0.01}),
+                "neg_scale": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step": 0.01}),
+            },
+            "optional": {
+                "uncond_zero_scale": ("FLOAT", {"default": 0.0, "max": 2.0, "step": 0.01}),
+                "cfg_start_pct": ("FLOAT", {"default": 0.0, "max": 1.0, "step": 0.01}),
+            },
+        }
+
+    RETURN_TYPES = ("GUIDER",)
+    FUNCTION = "get_guider"
+
+    CATEGORY = "sampling/custom_sampling/guiders"
+
+    def get_guider(
+        self,
+        model,
+        positive,
+        negative,
+        empty_conditioning,
+        threshold,
+        cfg,
+        neg_scale,
+        uncond_zero_scale=0.0,
+        cfg_start_pct=0.0,
+    ):
+        cfg_start_timestep = model.get_model_object("model_sampling").percent_to_sigma(cfg_start_pct)
+        g = Guider_PerpNegAG(model)
+        g.set_conds(positive, negative, empty_conditioning)
+        g.set_threshold(threshold, cfg_start_timestep)
+        g.set_uncond_zero_scale(uncond_zero_scale)
+        g.set_cfg(cfg, neg_scale)
+
+        return (g,)
+
+
+def project(a, b):
+    dtype = a.dtype
+    a, b = a.double(), b.double()
+    b = torch.nn.functional.normalize(b, dim=[-1, -2, -3])
+    a_par = (a * b).sum(dim=[-1, -2, -3], keepdim=True) * b
+    a_orth = a - a_par
+    return a_par.to(dtype), a_orth.to(dtype)
+
+
+class AdaptiveProjectedGuidanceFunction:
+    def __init__(self, momentum, eta, norm_threshold, adaptive_momentum=0, mode="normal"):
+        self.eta = eta
+        self.norm_threshold = norm_threshold
+        self.current_step = 999.0
+        self.init_momentum = momentum
+        self.momentum = momentum
+        self.running_average = 0.0
+        self.mode = mode
+        self.adaptive_momentum = adaptive_momentum
+
+    def __call__(self, args):
+        if "denoised" == self.mode:
+            cond = args["cond_denoised"]
+            uncond = args["uncond_denoised"]
+        else:
+            cond = args["cond"]
+            uncond = args["uncond"]
+        cfg_scale = args["cond_scale"]
+        sigma = args["sigma"][0].item()
+        step = args["model"].model_sampling.timestep(args["sigma"])[0].item()
+        x_orig = args["input"]
+        if self.mode == "vpred":
+            sigma = step
+            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)
+
+        if self.current_step < step:
+            self.current_step = 999.0
+            self.running_average = 0.0
+            self.momentum = self.init_momentum
+        else:
+            scale = self.init_momentum
+            if self.adaptive_momentum > 0:
+                scale -= scale * (self.adaptive_momentum**4) * (1000 - step)
+                if self.init_momentum < 0 and scale > 0:
+                    scale = 0
+                elif self.init_momentum > 0 and scale < 0:
+                    scale = 0
+                self.momentum = scale
+
+        self.current_step = step
+
+        diff = cond - uncond
+
+        new_average = self.momentum * self.running_average
+        self.running_average = diff + new_average
+        diff = self.running_average
+
+        if self.norm_threshold > 0.0:
+            diff_norm = diff.norm(p=2, dim=[-1, -2, -3], keepdim=True)
+            scale_factor = torch.minimum(torch.ones_like(diff), self.norm_threshold / diff_norm)
+            diff = diff * scale_factor
+
+        diff_parallel, diff_orthogonal = project(diff, cond)
+
+        pred = cond + (cfg_scale - 1) * (diff_orthogonal + self.eta * diff_parallel)
+        if "denoised" == self.mode:
+            pred = x_orig - pred
+        elif "vpred" == self.mode:
+            pred = x_orig - (x - pred * sigma / (sigma * sigma + 1.0) ** 0.5)
+        return pred
+
+
+class AdaptiveProjectedGuidance:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {"model": ("MODEL",)},
+            "optional": {
+                "momentum": ("FLOAT", {"default": 0.5, "min": -1.0, "max": 1.0, "step": 0.01}),
+                "eta": ("FLOAT", {"default": 1, "min": 0.0, "max": 1.0, "step": 0.01}),
+                "norm_threshold": ("FLOAT", {"default": 15.0, "min": 0.0, "max": 50.0, "step": 0.1}),
+                "mode": (["normal", "denoised", "vpred"],),
+                "adaptive_momentum": ("FLOAT", {"default": 0.18, "min": 0, "max": 1.0, "step": 0.01}),
+            },
+        }
+
+    RETURN_TYPES = ("MODEL",)
+    FUNCTION = "apply"
+
+    CATEGORY = "_for_testing"
+
+    def apply(self, model, momentum=0.5, eta=1.0, norm_threshold=15.0, mode="normal", adaptive_momentum=0.18):
+        fn = AdaptiveProjectedGuidanceFunction(momentum, eta, norm_threshold, adaptive_momentum, mode)
+        m = model.clone()
+        m.set_model_sampler_cfg_function(fn)
+        return (m,)
+
+
 NODE_CLASS_MAPPINGS = {
-    "AdaptiveGuidance": AdaptiveGuidance,
-    "LinearAdaptiveGuidance": LinearAdaptiveGuidance,
+    "AdaptiveGuidance": AdaptiveGuidanceGuider,
+    "PerpNegAdaptiveGuidanceGuider": PerpNegAGGuider,
+    "AdaptiveProjectedGuidance": AdaptiveProjectedGuidance,
+}
+
+NODE_DISPLAY_NAME_MAPPINGS = {
+    "AdaptiveGuidance": "AdaptiveGuider",
+    "PerpNegAdaptiveGuidanceGuider": "PerpNegAdaptiveGuider",
 }

pyproject.toml

@@ -0,0 +1,14 @@
+[project]
+name = "comfyui-adaptive-guidance"
+description = "An implementation of adaptive guidance for ComfyUI\nSee https://bcv-uniandes.github.io/adaptiveguidance-wp/"
+version = "0.4.0"
+license = { text = "GNU General Public License v3.0" }
+
+[project.urls]
+Repository = "https://github.com/asagi4/ComfyUI-Adaptive-Guidance"
+#  Used by Comfy Registry https://comfyregistry.org
+
+[tool.comfy]
+PublisherId = "asagi4"
+DisplayName = "ComfyUI Adaptive Guidance"
+Icon = ""