Working RescaleCFG node.

This was broken because of recent changes so I fixed it and moved it from
the experiments repo.

comfy/samplers.py

@@ -248,7 +248,7 @@ def sampling_function(model_function, x, timestep, uncond, cond, cond_scale, mod
 
         cond, uncond = calc_cond_uncond_batch(model_function, cond, uncond, x, timestep, max_total_area, model_options)
         if "sampler_cfg_function" in model_options:
-            args = {"cond": x - cond, "uncond": x - uncond, "cond_scale": cond_scale, "timestep": timestep, "input": x}
+            args = {"cond": x - cond, "uncond": x - uncond, "cond_scale": cond_scale, "timestep": timestep, "input": x, "sigma": timestep}
             return x - model_options["sampler_cfg_function"](args)
         else:
             return uncond + (cond - uncond) * cond_scale

comfy_extras/nodes_model_advanced.py

@@ -123,6 +123,45 @@ class ModelSamplingDiscrete:
         m.add_object_patch("model_sampling", model_sampling)
         return (m, )
 
+class RescaleCFG:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": { "model": ("MODEL",),
+                              "multiplier": ("FLOAT", {"default": 0.7, "min": 0.0, "max": 1.0, "step": 0.01}),
+                              }}
+    RETURN_TYPES = ("MODEL",)
+    FUNCTION = "patch"
+
+    CATEGORY = "advanced/model"
+
+    def patch(self, model, multiplier):
+        def rescale_cfg(args):
+            cond = args["cond"]
+            uncond = args["uncond"]
+            cond_scale = args["cond_scale"]
+            sigma = args["sigma"]
+            x_orig = args["input"]
+
+            #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)
+
+            #rescalecfg
+            x_cfg = uncond + cond_scale * (cond - uncond)
+            ro_pos = torch.std(cond, dim=(1,2,3), keepdim=True)
+            ro_cfg = torch.std(x_cfg, dim=(1,2,3), keepdim=True)
+
+            x_rescaled = x_cfg * (ro_pos / ro_cfg)
+            x_final = multiplier * x_rescaled + (1.0 - multiplier) * x_cfg
+
+            return x_orig - (x - x_final * sigma / (sigma * sigma + 1.0) ** 0.5)
+
+        m = model.clone()
+        m.set_model_sampler_cfg_function(rescale_cfg)
+        return (m, )
+
 NODE_CLASS_MAPPINGS = {
     "ModelSamplingDiscrete": ModelSamplingDiscrete,
+    "RescaleCFG": RescaleCFG,
 }