debug2

layers.py

@@ -227,6 +227,7 @@ def __init__(self, context_dim, hidden_dim, ip_hidden_states=None, ip_scale=None
         self.ip_hidden_states = ip_hidden_states
         self.ip_scale = ip_scale
         self.neg_hidden_states = None
+        self.in_hidden_states_pos = None
         # Ensure context_dim matches the dimension of ip_hidden_states
         self.context_dim = context_dim
         self.hidden_dim = hidden_dim

nodes.py

@@ -510,6 +510,7 @@ def applymodel(self, model, ip_adapter_flux, image, strength_model):
             ipad.load_state_dict(block.state_dict())
             ipad.to(dtype=torch.bfloat16)
             ipad.neg_hidden_states = ip_neg_pr
+            ipad.in_hidden_states_pos = ip_projes
             npp = DoubleStreamMixerProcessor()
             npp.add_ipadapter(ipad)
             ipad_blocks.append(npp)

sampling.py

@@ -40,9 +40,14 @@ def model_forward(
         controlnet_depth = len(block_controlnet_hidden_states)
     for index_block, block in enumerate(model.double_blocks):
         if isinstance(block.processor, DoubleStreamMixerProcessor):
-            if not neg_mode is None:
+            if neg_mode:
                 for ip in block.processor.ip_adapters:
                     ip.ip_hidden_states = ip.neg_ip_hidden_states
+            else:
+                for ip in block.processor.ip_adapters:
+                    ip.ip_hidden_states = ip.in_hidden_states_pos
+        img, txt = block(img=img, txt=txt, vec=vec, pe=pe)
+
         img, txt = block(img=img, txt=txt, vec=vec, pe=pe)
         # controlnet residual
 
@@ -299,7 +304,7 @@ def denoise_controlnet(
                 y=neg_vec,
                 timesteps=t_vec,
                 guidance=guidance_vec,
-                block_controlnet_hidden_states=[i * controlnet_gs for i in neg_block_res_samples]
+                block_controlnet_hidden_states=[i * controlnet_gs for i in neg_block_res_samples],
                 neg_mode = True,
 
             )