works

comfy/ldm/seedvr/model.py

@@ -1321,6 +1321,14 @@ class NaDiT(nn.Module):
                 layers=["out"],
                 modes=["in"],
             )
+    
+        self.stop_cfg_index = -1
+
+    def set_cfg_stop_index(self, cfg):
+        self.stop_cfg_index = cfg
+
+    def get_cfg_stop_index(self):
+        return self.stop_cfg_index
 
     def forward(
         self,
@@ -1335,14 +1343,17 @@ class NaDiT(nn.Module):
         blocks_replace = patches_replace.get("dit", {})
         conditions = kwargs.get("condition")
 
-        pos_cond, neg_cond = context.chunk(2, dim=0)
-        pos_cond, neg_cond = pos_cond.squeeze(0), neg_cond.squeeze(0)
-        txt, txt_shape = flatten([pos_cond, neg_cond])
+        try:
+            neg_cond, pos_cond = context.chunk(2, dim=0)
+            pos_cond, neg_cond = pos_cond.squeeze(0), neg_cond.squeeze(0)
+            txt, txt_shape = flatten([pos_cond, neg_cond])
+        except:
+            txt, txt_shape = flatten([context.squeeze(0)])
 
         vid, vid_shape = flatten(x)
         cond_latent, _ = flatten(conditions)
 
-        vid = torch.cat([cond_latent, vid], dim=-1)
+        vid = torch.cat([vid, cond_latent], dim=-1)
         if txt_shape.size(-1) == 1 and self.need_txt_repeat:
             txt, txt_shape = repeat(txt, txt_shape, "l c -> t l c", t=vid_shape[:, 0])
 
@@ -1404,4 +1415,9 @@ class NaDiT(nn.Module):
 
         vid, vid_shape = self.vid_out(vid, vid_shape, cache, vid_shape_before_patchify = vid_shape_before_patchify)
         vid = unflatten(vid, vid_shape)
-        return torch.stack(vid)
+        out =  torch.stack(vid)
+        try:
+            pos, neg = out.chunk(2)
+            return torch.cat([neg, pos])
+        except:
+            return out

comfy/samplers.py

@@ -947,8 +947,21 @@ class CFGGuider:
 
     def __call__(self, *args, **kwargs):
         return self.predict_noise(*args, **kwargs)
+    
+    def handle_dynamic_cfg(self, timestep, model_options):
+        if hasattr(self.model_patcher.model.diffusion_model, "stop_cfg_index"):
+            stop_index = self.model_patcher.model.diffusion_model.stop_cfg_index
+            transformer_options = model_options.get("transformer_options", {})
+            sigmas = transformer_options.get("sample_sigmas", None)            
+            if sigmas is not None or self.cfg != 1.0:
+                dist = torch.abs(sigmas - timestep)
+                i = torch.argmin(dist).item()
+
+                if stop_index == i or (stop_index == -1 and i == len(sigmas) - 2):
+                    self.set_cfg(1.0)
 
     def predict_noise(self, x, timestep, model_options={}, seed=None):
+        self.handle_dynamic_cfg(timestep, model_options)
         return sampling_function(self.inner_model, x, timestep, self.conds.get("negative", None), self.conds.get("positive", None), self.cfg, model_options=model_options, seed=seed)
 
     def inner_sample(self, noise, latent_image, device, sampler, sigmas, denoise_mask, callback, disable_pbar, seed):

comfy_extras/nodes_model_advanced.py

@@ -5,6 +5,22 @@ import nodes
 import torch
 import node_helpers
 
+class CFGCutoff:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {"model": ("MODEL",), "cfg_stop_index": ("INT", {"default": -1, "min": -1, })}}
+
+    RETURN_TYPES = ("MODEL",)
+    FUNCTION = "patch"
+
+    CATEGORY = "advanced/model"
+
+    def patch(self, model, cfg_stop_index):
+        diff_model = model.model.diffusion_model
+        if hasattr(diff_model, "set_cfg_stop_index"):
+            diff_model.set_cfg_stop_index(cfg_stop_index)
+
+        return (model,)
 
 class LCM(comfy.model_sampling.EPS):
     def calculate_denoised(self, sigma, model_output, model_input):
@@ -326,4 +342,5 @@ NODE_CLASS_MAPPINGS = {
     "ModelSamplingFlux": ModelSamplingFlux,
     "RescaleCFG": RescaleCFG,
     "ModelComputeDtype": ModelComputeDtype,
+    "CFGCutoff": CFGCutoff
 }