Support loading controlnets with different input.

Author: comfyanonymous

comfy/controlnet.py

@@ -449,7 +449,9 @@ def load_controlnet_flux_instantx(sd):
     if union_cnet in new_sd:
         num_union_modes = new_sd[union_cnet].shape[0]
 
-    control_model = comfy.ldm.flux.controlnet.ControlNetFlux(latent_input=True, num_union_modes=num_union_modes, operations=operations, device=offload_device, dtype=unet_dtype, **model_config.unet_config)
+    control_latent_channels = new_sd.get("pos_embed_input.weight").shape[1] // 4
+
+    control_model = comfy.ldm.flux.controlnet.ControlNetFlux(latent_input=True, num_union_modes=num_union_modes, control_latent_channels=control_latent_channels, operations=operations, device=offload_device, dtype=unet_dtype, **model_config.unet_config)
     control_model = controlnet_load_state_dict(control_model, new_sd)
 
     latent_format = comfy.latent_formats.Flux()

comfy/ldm/flux/controlnet.py

@@ -52,7 +52,7 @@ def forward(self, x):
 
 
 class ControlNetFlux(Flux):
-    def __init__(self, latent_input=False, num_union_modes=0, mistoline=False, image_model=None, dtype=None, device=None, operations=None, **kwargs):
+    def __init__(self, latent_input=False, num_union_modes=0, mistoline=False, control_latent_channels=None, image_model=None, dtype=None, device=None, operations=None, **kwargs):
         super().__init__(final_layer=False, dtype=dtype, device=device, operations=operations, **kwargs)
 
         self.main_model_double = 19
@@ -80,7 +80,12 @@ def __init__(self, latent_input=False, num_union_modes=0, mistoline=False, image
 
         self.gradient_checkpointing = False
         self.latent_input = latent_input
-        self.pos_embed_input = operations.Linear(self.in_channels, self.hidden_size, bias=True, dtype=dtype, device=device)
+        if control_latent_channels is None:
+            control_latent_channels = self.in_channels
+        else:
+            control_latent_channels *= 2 * 2 #patch size
+
+        self.pos_embed_input = operations.Linear(control_latent_channels, self.hidden_size, bias=True, dtype=dtype, device=device)
         if not self.latent_input:
             if self.mistoline:
                 self.input_cond_block = MistolineCondDownsamplBlock(dtype=dtype, device=device, operations=operations)