Bump version

Add perpneg-adaptive-guidance guider

__init__.py

@@ -1,10 +1,11 @@
 import comfy.samplers
+import comfy_extras.nodes_perpneg
 import torch
 
 cos = torch.nn.CosineSimilarity(dim=1)
 
 
-class AdaptiveGuider(comfy.samplers.CFGGuider):
+class Guider_AdaptiveGuidance(comfy.samplers.CFGGuider):
     threshold_timestep = 0
     uz_scale = 0.0
 
@@ -43,7 +44,7 @@ class AdaptiveGuider(comfy.samplers.CFGGuider):
             # Is this reshape correct? It at least gives a scalar value...
             sim = cos(cond_pred.reshape(1, -1), uncond_pred.reshape(1, -1)).item()
             if sim >= self.threshold:
-                print("AdaptiveGuidance: Cosine similarity", sim, "exceeds threshold, setting CFG to 1.0")
+                print(f"\nAdaptiveGuidance: Cosine similarity {sim:.4f} exceeds threshold, setting CFG to 1.0")
                 self.threshold_timestep = ts
         return comfy.samplers.cfg_function(
             self.inner_model,
@@ -58,7 +59,7 @@ class AdaptiveGuider(comfy.samplers.CFGGuider):
         )
 
 
-class AdaptiveGuidance:
+class AdaptiveGuidanceGuider:
     @classmethod
     def INPUT_TYPES(s):
         return {
@@ -73,12 +74,12 @@ class AdaptiveGuidance:
         }
 
     RETURN_TYPES = ("GUIDER",)
-    FUNCTION = "patch"
+    FUNCTION = "get_guider"
 
     CATEGORY = "sampling/custom_sampling/guiders"
 
-    def patch(self, model, positive, negative, threshold, cfg, uncond_zero_scale=0.0):
-        g = AdaptiveGuider(model)
+    def get_guider(self, model, positive, negative, threshold, cfg, uncond_zero_scale=0.0):
+        g = Guider_AdaptiveGuidance(model)
         g.set_conds(positive, negative)
         g.set_threshold(threshold)
         g.set_uncond_zero_scale(uncond_zero_scale)
@@ -87,4 +88,112 @@ class AdaptiveGuidance:
         return (g,)
 
 
-NODE_CLASS_MAPPINGS = {"AdaptiveGuidance": AdaptiveGuidance}
+class Guider_PerpNegAG(comfy_extras.nodes_perpneg.Guider_PerpNeg):
+    threshold_timestep = 0
+    uz_scale = 0.0
+
+    def set_threshold(self, threshold):
+        self.threshold = threshold
+
+    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 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 self.uz_scale > 0.0:
+                model_options = model_options.copy()
+                model_options["sampler_cfg_function"] = self.zero_cond
+            return comfy.samplers.sampling_function(
+                self.inner_model, x, timestep, uncond, cond, 1.0, model_options=model_options, seed=seed
+            )
+        self.threshold_timestep = 0
+
+        # From comfy_extras.nodes_perpneg - Guider_PerpNeg
+        # No need for calculating perp-neg when skipping negative
+        empty_cond = self.conds.get("empty_negative_prompt")
+        (cond_pred, uncond_pred, empty_cond_pred) = comfy.samplers.calc_cond_batch(
+            self.inner_model, [cond, uncond, empty_cond], x, timestep, model_options
+        )
+        cfg_result = comfy_extras.nodes_perpneg.perp_neg(
+            x, cond_pred, uncond_pred, empty_cond_pred, self.neg_scale, self.cfg
+        )
+
+        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"\nPerpNegAG: Cosine similarity {sim:.4f} exceeds threshold, setting CFG to 1.0")
+                self.threshold_timestep = ts
+
+        # From comfy_extras.nodes_perpneg - Guider_PerpNeg
+        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 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": 0.001, "round": 0.001}),
+                "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})},
+        }
+
+    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
+    ):
+        g = Guider_PerpNegAG(model)
+        g.set_conds(positive, negative, empty_conditioning)
+        g.set_threshold(threshold)
+        g.set_uncond_zero_scale(uncond_zero_scale)
+        g.set_cfg(cfg, neg_scale)
+
+        return (g,)
+
+
+NODE_CLASS_MAPPINGS = {
+    "AdaptiveGuidance": AdaptiveGuidanceGuider,
+    "PerpNegAdaptiveGuidanceGuider": PerpNegAGGuider,
+}
+
+NODE_DISPLAY_NAME_MAPPINGS = {
+    "AdaptiveGuidance": "AdaptiveGuider",
+    "PerpNegAdaptiveGuidanceGuider": "PerpNegAdaptiveGuider",
+}

pyproject.toml

@@ -1,8 +1,8 @@
 [project]
 name = "comfyui-adaptive-guidance"
 description = "An implementation of adaptive guidance for ComfyUI\nSee https://bcv-uniandes.github.io/adaptiveguidance-wp/"
-version = "0.1.0"
-license = "GPL-3.0"
+version = "0.2.0"
+license = { text = "GNU General Public License v3.0" }
 
 [project.urls]
 Repository = "https://github.com/asagi4/ComfyUI-Adaptive-Guidance"