Implement Perp-Neg

comfy/samplers.py

@@ -251,7 +251,8 @@ def sampling_function(model, x, timestep, uncond, cond, cond_scale, model_option
 
         cond_pred, uncond_pred = calc_cond_uncond_batch(model, cond, uncond_, x, timestep, model_options)
         if "sampler_cfg_function" in model_options:
-            args = {"cond": x - cond_pred, "uncond": x - uncond_pred, "cond_scale": cond_scale, "timestep": timestep, "input": x, "sigma": timestep}
+            args = {"cond": x - cond_pred, "uncond": x - uncond_pred, "cond_scale": cond_scale, "timestep": timestep, "input": x, "sigma": timestep,
+                    "cond_denoised": cond_pred, "uncond_denoised": uncond_pred, "model": model, "model_options": model_options}
             cfg_result = x - model_options["sampler_cfg_function"](args)
         else:
             cfg_result = uncond_pred + (cond_pred - uncond_pred) * cond_scale

comfy_extras/nodes_perpneg.py

+import torch
+import comfy.model_management
+import comfy.sample
+import comfy.samplers
+import comfy.utils
+
+
+class PerpNeg:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {"model": ("MODEL", ),
+                             "clip": ("CLIP", ),
+                             "neg_scale": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0}),
+                            }}
+    RETURN_TYPES = ("MODEL",)
+    FUNCTION = "patch"
+
+    CATEGORY = "_for_testing"
+
+    def patch(self, model, clip, neg_scale):
+        m = model.clone()
+
+        tokens = clip.tokenize("")
+        nocond, nocond_pooled = clip.encode_from_tokens(tokens, return_pooled=True)
+        nocond = [[nocond, {"pooled_output": nocond_pooled}]]
+        nocond = comfy.sample.convert_cond(nocond)
+
+        def cfg_function(args):
+            model = args["model"]
+            noise_pred_pos = args["cond_denoised"]
+            noise_pred_neg = args["uncond_denoised"]
+            cond_scale = args["cond_scale"]
+            x = args["input"]
+            sigma = args["sigma"]
+            model_options = args["model_options"]
+            
+            (noise_pred_nocond, _) = comfy.samplers.calc_cond_uncond_batch(model, nocond, None, x, sigma, model_options)
+            
+            pos = noise_pred_pos - noise_pred_nocond
+            neg = noise_pred_neg - noise_pred_nocond
+            perp = ((torch.mul(pos, neg).sum())/(torch.norm(neg)**2)) * neg
+            perp_neg = perp * neg_scale
+            cfg_result = noise_pred_nocond + cond_scale*(pos - perp_neg)
+            cfg_result = x - cfg_result
+            return cfg_result
+
+        m.set_model_sampler_cfg_function(cfg_function)
+
+        return (m, )
+
+
+NODE_CLASS_MAPPINGS = {
+    "PerpNeg": PerpNeg,
+}
+
+NODE_DISPLAY_NAME_MAPPINGS = {
+    "PerpNeg": "Perp-Neg",
+}

nodes.py

@@ -1868,6 +1868,7 @@ def init_custom_nodes():
         "nodes_images.py",
         "nodes_video_model.py",
         "nodes_sag.py",
+        "nodes_perpneg.py",
     ]
 
     for node_file in extras_files: