Support AliMama SD3 and Flux inpaint controlnets.

Use the ControlNetInpaintingAliMamaApply node.

comfy/cldm/mmdit.py

@@ -6,6 +6,7 @@ class ControlNet(comfy.ldm.modules.diffusionmodules.mmdit.MMDiT):
     def __init__(
         self,
         num_blocks = None,
+        control_latent_channels = None,
         dtype = None,
         device = None,
         operations = None,
@@ -17,10 +18,13 @@ def __init__(
         for _ in range(len(self.joint_blocks)):
             self.controlnet_blocks.append(operations.Linear(self.hidden_size, self.hidden_size, device=device, dtype=dtype))
 
+        if control_latent_channels is None:
+            control_latent_channels = self.in_channels
+
         self.pos_embed_input = comfy.ldm.modules.diffusionmodules.mmdit.PatchEmbed(
             None,
             self.patch_size,
-            self.in_channels,
+            control_latent_channels,
             self.hidden_size,
             bias=True,
             strict_img_size=False,

comfy/controlnet.py

@@ -79,13 +79,19 @@ def __init__(self, device=None):
         self.previous_controlnet = None
         self.extra_conds = []
         self.strength_type = StrengthType.CONSTANT
+        self.concat_mask = False
+        self.extra_concat_orig = []
+        self.extra_concat = None
 
-    def set_cond_hint(self, cond_hint, strength=1.0, timestep_percent_range=(0.0, 1.0), vae=None):
+    def set_cond_hint(self, cond_hint, strength=1.0, timestep_percent_range=(0.0, 1.0), vae=None, extra_concat=[]):
         self.cond_hint_original = cond_hint
         self.strength = strength
         self.timestep_percent_range = timestep_percent_range
         if self.latent_format is not None:
             self.vae = vae
+        self.extra_concat_orig = extra_concat.copy()
+        if self.concat_mask and len(self.extra_concat_orig) == 0:
+            self.extra_concat_orig.append(torch.tensor([[[[1.0]]]]))
         return self
 
     def pre_run(self, model, percent_to_timestep_function):
@@ -100,9 +106,9 @@ def set_previous_controlnet(self, controlnet):
     def cleanup(self):
         if self.previous_controlnet is not None:
             self.previous_controlnet.cleanup()
-        if self.cond_hint is not None:
-            del self.cond_hint
-            self.cond_hint = None
+
+        self.cond_hint = None
+        self.extra_concat = None
         self.timestep_range = None
 
     def get_models(self):
@@ -123,6 +129,8 @@ def copy_to(self, c):
         c.vae = self.vae
         c.extra_conds = self.extra_conds.copy()
         c.strength_type = self.strength_type
+        c.concat_mask = self.concat_mask
+        c.extra_concat_orig = self.extra_concat_orig.copy()
 
     def inference_memory_requirements(self, dtype):
         if self.previous_controlnet is not None:
@@ -175,7 +183,7 @@ def set_extra_arg(self, argument, value=None):
 
 
 class ControlNet(ControlBase):
-    def __init__(self, control_model=None, global_average_pooling=False, compression_ratio=8, latent_format=None, device=None, load_device=None, manual_cast_dtype=None, extra_conds=["y"], strength_type=StrengthType.CONSTANT):
+    def __init__(self, control_model=None, global_average_pooling=False, compression_ratio=8, latent_format=None, device=None, load_device=None, manual_cast_dtype=None, extra_conds=["y"], strength_type=StrengthType.CONSTANT, concat_mask=False):
         super().__init__(device)
         self.control_model = control_model
         self.load_device = load_device
@@ -189,6 +197,7 @@ def __init__(self, control_model=None, global_average_pooling=False, compression
         self.latent_format = latent_format
         self.extra_conds += extra_conds
         self.strength_type = strength_type
+        self.concat_mask = concat_mask
 
     def get_control(self, x_noisy, t, cond, batched_number):
         control_prev = None
@@ -220,6 +229,13 @@ def get_control(self, x_noisy, t, cond, batched_number):
                 comfy.model_management.load_models_gpu(loaded_models)
             if self.latent_format is not None:
                 self.cond_hint = self.latent_format.process_in(self.cond_hint)
+            if len(self.extra_concat_orig) > 0:
+                to_concat = []
+                for c in self.extra_concat_orig:
+                    c = comfy.utils.common_upscale(c, self.cond_hint.shape[3], self.cond_hint.shape[2], self.upscale_algorithm, "center")
+                    to_concat.append(comfy.utils.repeat_to_batch_size(c, self.cond_hint.shape[0]))
+                self.cond_hint = torch.cat([self.cond_hint] + to_concat, dim=1)
+
             self.cond_hint = self.cond_hint.to(device=self.device, dtype=dtype)
         if x_noisy.shape[0] != self.cond_hint.shape[0]:
             self.cond_hint = broadcast_image_to(self.cond_hint, x_noisy.shape[0], batched_number)
@@ -410,12 +426,17 @@ def load_controlnet_mmdit(sd):
     for k in sd:
         new_sd[k] = sd[k]
 
-    control_model = comfy.cldm.mmdit.ControlNet(num_blocks=num_blocks, operations=operations, device=offload_device, dtype=unet_dtype, **model_config.unet_config)
+    concat_mask = False
+    control_latent_channels = new_sd.get("pos_embed_input.proj.weight").shape[1]
+    if control_latent_channels == 17: #inpaint controlnet
+        concat_mask = True
+
+    control_model = comfy.cldm.mmdit.ControlNet(num_blocks=num_blocks, 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.SD3()
     latent_format.shift_factor = 0 #SD3 controlnet weirdness
-    control = ControlNet(control_model, compression_ratio=1, latent_format=latent_format, load_device=load_device, manual_cast_dtype=manual_cast_dtype)
+    control = ControlNet(control_model, compression_ratio=1, latent_format=latent_format, concat_mask=concat_mask, load_device=load_device, manual_cast_dtype=manual_cast_dtype)
     return control
 
 
@@ -450,13 +471,16 @@ def load_controlnet_flux_instantx(sd):
         num_union_modes = new_sd[union_cnet].shape[0]
 
     control_latent_channels = new_sd.get("pos_embed_input.weight").shape[1] // 4
+    concat_mask = False
+    if control_latent_channels == 17:
+        concat_mask = True
 
     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()
     extra_conds = ['y', 'guidance']
-    control = ControlNet(control_model, compression_ratio=1, latent_format=latent_format, load_device=load_device, manual_cast_dtype=manual_cast_dtype, extra_conds=extra_conds)
+    control = ControlNet(control_model, compression_ratio=1, latent_format=latent_format, concat_mask=concat_mask, load_device=load_device, manual_cast_dtype=manual_cast_dtype, extra_conds=extra_conds)
     return control
 
 def convert_mistoline(sd):

comfy_extras/nodes_controlnet.py

@@ -1,4 +1,6 @@
 from comfy.cldm.control_types import UNION_CONTROLNET_TYPES
+import nodes
+import comfy.utils
 
 class SetUnionControlNetType:
     @classmethod
@@ -22,6 +24,33 @@ def set_controlnet_type(self, control_net, type):
 
         return (control_net,)
 
+class ControlNetInpaintingAliMamaApply(nodes.ControlNetApplyAdvanced):
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {"positive": ("CONDITIONING", ),
+                             "negative": ("CONDITIONING", ),
+                             "control_net": ("CONTROL_NET", ),
+                             "vae": ("VAE", ),
+                             "image": ("IMAGE", ),
+                             "mask": ("MASK", ),
+                             "strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}),
+                             "start_percent": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.001}),
+                             "end_percent": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.001})
+                             }}
+
+    FUNCTION = "apply_inpaint_controlnet"
+
+    CATEGORY = "conditioning/controlnet"
+
+    def apply_inpaint_controlnet(self, positive, negative, control_net, vae, image, mask, strength, start_percent, end_percent):
+        mask = 1.0 - mask.reshape((-1, 1, mask.shape[-2], mask.shape[-1]))
+        mask_apply = comfy.utils.common_upscale(mask, image.shape[2], image.shape[1], "bilinear", "center").round()
+        image = image * mask_apply.movedim(1, -1).repeat(1, 1, 1, image.shape[3])
+        return self.apply_controlnet(positive, negative, control_net, image, strength, start_percent, end_percent, vae=vae, extra_concat=[mask])
+
+
+
 NODE_CLASS_MAPPINGS = {
     "SetUnionControlNetType": SetUnionControlNetType,
+    "ControlNetInpaintingAliMamaApply": ControlNetInpaintingAliMamaApply,
 }

nodes.py

@@ -824,7 +824,7 @@ def INPUT_TYPES(s):
 
     CATEGORY = "conditioning/controlnet"
 
-    def apply_controlnet(self, positive, negative, control_net, image, strength, start_percent, end_percent, vae=None):
+    def apply_controlnet(self, positive, negative, control_net, image, strength, start_percent, end_percent, vae=None, extra_concat=[]):
         if strength == 0:
             return (positive, negative)
 
@@ -841,7 +841,7 @@ def apply_controlnet(self, positive, negative, control_net, image, strength, sta
                 if prev_cnet in cnets:
                     c_net = cnets[prev_cnet]
                 else:
-                    c_net = control_net.copy().set_cond_hint(control_hint, strength, (start_percent, end_percent), vae)
+                    c_net = control_net.copy().set_cond_hint(control_hint, strength, (start_percent, end_percent), vae=vae, extra_concat=extra_concat)
                     c_net.set_previous_controlnet(prev_cnet)
                     cnets[prev_cnet] = c_net