Controlnet code refactor.

comfy/controlnet.py

@@ -191,13 +191,16 @@ class ControlNet(ControlBase):
             self.cond_hint = broadcast_image_to(self.cond_hint, x_noisy.shape[0], batched_number)
 
         context = cond.get('crossattn_controlnet', cond['c_crossattn'])
-        y = cond.get('y', None)
-        if y is not None:
-            y = y.to(dtype)
+        extra = self.extra_args.copy()
+        for c in ["y", "guidance"]: #TODO
+            temp = cond.get(c, None)
+            if temp is not None:
+                extra[c] = temp.to(dtype)
+
         timestep = self.model_sampling_current.timestep(t)
         x_noisy = self.model_sampling_current.calculate_input(t, x_noisy)
 
-        control = self.control_model(x=x_noisy.to(dtype), hint=self.cond_hint, timesteps=timestep.float(), context=context.to(dtype), y=y, **self.extra_args)
+        control = self.control_model(x=x_noisy.to(dtype), hint=self.cond_hint, timesteps=timestep.to(dtype), context=context.to(dtype), **extra)
         return self.control_merge(control, control_prev, output_dtype)
 
     def copy(self):
@@ -338,12 +341,8 @@ class ControlLora(ControlNet):
     def inference_memory_requirements(self, dtype):
         return comfy.utils.calculate_parameters(self.control_weights) * comfy.model_management.dtype_size(dtype) + ControlBase.inference_memory_requirements(self, dtype)
 
-def load_controlnet_mmdit(sd):
-    new_sd = comfy.model_detection.convert_diffusers_mmdit(sd, "")
-    model_config = comfy.model_detection.model_config_from_unet(new_sd, "", True)
-    num_blocks = comfy.model_detection.count_blocks(new_sd, 'joint_blocks.{}.')
-    for k in sd:
-        new_sd[k] = sd[k]
+def controlnet_config(sd):
+    model_config = comfy.model_detection.model_config_from_unet(sd, "", True)
 
     supported_inference_dtypes = model_config.supported_inference_dtypes
 
@@ -356,14 +355,27 @@ def load_controlnet_mmdit(sd):
     else:
         operations = comfy.ops.disable_weight_init
 
-    control_model = comfy.cldm.mmdit.ControlNet(num_blocks=num_blocks, operations=operations, device=load_device, dtype=unet_dtype, **controlnet_config)
-    missing, unexpected = control_model.load_state_dict(new_sd, strict=False)
+    return model_config, operations, load_device, unet_dtype, manual_cast_dtype
+
+def controlnet_load_state_dict(control_model, sd):
+    missing, unexpected = control_model.load_state_dict(sd, strict=False)
 
     if len(missing) > 0:
         logging.warning("missing controlnet keys: {}".format(missing))
 
     if len(unexpected) > 0:
         logging.debug("unexpected controlnet keys: {}".format(unexpected))
+    return control_model
+
+def load_controlnet_mmdit(sd):
+    new_sd = comfy.model_detection.convert_diffusers_mmdit(sd, "")
+    model_config, operations, load_device, unet_dtype, manual_cast_dtype = controlnet_config(new_sd)
+    num_blocks = comfy.model_detection.count_blocks(new_sd, 'joint_blocks.{}.')
+    for k in sd:
+        new_sd[k] = sd[k]
+
+    control_model = comfy.cldm.mmdit.ControlNet(num_blocks=num_blocks, operations=operations, device=load_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

comfy/model_detection.py

@@ -137,8 +137,8 @@ def detect_unet_config(state_dict, key_prefix):
         dit_config["hidden_size"] = 3072
         dit_config["mlp_ratio"] = 4.0
         dit_config["num_heads"] = 24
-        dit_config["depth"] = 19
-        dit_config["depth_single_blocks"] = 38
+        dit_config["depth"] = count_blocks(state_dict_keys, '{}double_blocks.'.format(key_prefix) + '{}.')
+        dit_config["depth_single_blocks"] = count_blocks(state_dict_keys, '{}single_blocks.'.format(key_prefix) + '{}.')
         dit_config["axes_dim"] = [16, 56, 56]
         dit_config["theta"] = 10000
         dit_config["qkv_bias"] = True