[pull] master from comfyanonymous:master

comfy_extras/nodes_model_patch.py

@@ -35,6 +35,7 @@ def __init__(
         device=None, dtype=None, operations=None
     ):
         super().__init__()
+        self.additional_in_dim = additional_in_dim
         self.img_in = operations.Linear(in_dim + additional_in_dim, dim, device=device, dtype=dtype)
         self.controlnet_blocks = torch.nn.ModuleList(
             [
@@ -44,7 +45,7 @@ def __init__(
         )
 
     def process_input_latent_image(self, latent_image):
-        latent_image = comfy.latent_formats.Wan21().process_in(latent_image)
+        latent_image[:, :16] = comfy.latent_formats.Wan21().process_in(latent_image[:, :16])
         patch_size = 2
         hidden_states = comfy.ldm.common_dit.pad_to_patch_size(latent_image, (1, patch_size, patch_size))
         orig_shape = hidden_states.shape
@@ -73,19 +74,33 @@ def load_model_patch(self, name):
         sd = comfy.utils.load_torch_file(model_patch_path, safe_load=True)
         dtype = comfy.utils.weight_dtype(sd)
         # TODO: this node will work with more types of model patches
-        model = QwenImageBlockWiseControlNet(device=comfy.model_management.unet_offload_device(), dtype=dtype, operations=comfy.ops.manual_cast)
+        additional_in_dim = sd["img_in.weight"].shape[1] - 64
+        model = QwenImageBlockWiseControlNet(additional_in_dim=additional_in_dim, device=comfy.model_management.unet_offload_device(), dtype=dtype, operations=comfy.ops.manual_cast)
         model.load_state_dict(sd)
         model = comfy.model_patcher.ModelPatcher(model, load_device=comfy.model_management.get_torch_device(), offload_device=comfy.model_management.unet_offload_device())
         return (model,)
 
 
 class DiffSynthCnetPatch:
-    def __init__(self, model_patch, vae, image, strength):
-        self.encoded_image = model_patch.model.process_input_latent_image(vae.encode(image))
+    def __init__(self, model_patch, vae, image, strength, mask=None):
         self.model_patch = model_patch
         self.vae = vae
         self.image = image
         self.strength = strength
+        self.mask = mask
+        self.encoded_image = model_patch.model.process_input_latent_image(self.encode_latent_cond(image))
+
+    def encode_latent_cond(self, image):
+        latent_image = self.vae.encode(image)
+        if self.model_patch.model.additional_in_dim > 0:
+            if self.mask is None:
+                mask_ = torch.ones_like(latent_image)[:, :self.model_patch.model.additional_in_dim // 4]
+            else:
+                mask_ = comfy.utils.common_upscale(self.mask.mean(dim=1, keepdim=True), latent_image.shape[-1], latent_image.shape[-2], "bilinear", "none")
+
+            return torch.cat([latent_image, mask_], dim=1)
+        else:
+            return latent_image
 
     def __call__(self, kwargs):
         x = kwargs.get("x")
@@ -95,7 +110,7 @@ def __call__(self, kwargs):
             spacial_compression = self.vae.spacial_compression_encode()
             image_scaled = comfy.utils.common_upscale(self.image.movedim(-1, 1), x.shape[-1] * spacial_compression, x.shape[-2] * spacial_compression, "area", "center")
             loaded_models = comfy.model_management.loaded_models(only_currently_used=True)
-            self.encoded_image = self.model_patch.model.process_input_latent_image(self.vae.encode(image_scaled.movedim(1, -1)))
+            self.encoded_image = self.model_patch.model.process_input_latent_image(self.encode_latent_cond(image_scaled.movedim(1, -1)))
             comfy.model_management.load_models_gpu(loaded_models)
 
         img = img + (self.model_patch.model.control_block(img, self.encoded_image.to(img.dtype), block_index) * self.strength)
@@ -118,17 +133,25 @@ def INPUT_TYPES(s):
                               "vae": ("VAE",),
                               "image": ("IMAGE",),
                               "strength": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}),
-                              }}
+                              },
+                "optional": {"mask": ("MASK",)}}
     RETURN_TYPES = ("MODEL",)
     FUNCTION = "diffsynth_controlnet"
     EXPERIMENTAL = True
 
     CATEGORY = "advanced/loaders/qwen"
 
-    def diffsynth_controlnet(self, model, model_patch, vae, image, strength):
+    def diffsynth_controlnet(self, model, model_patch, vae, image, strength, mask=None):
         model_patched = model.clone()
         image = image[:, :, :, :3]
-        model_patched.set_model_double_block_patch(DiffSynthCnetPatch(model_patch, vae, image, strength))
+        if mask is not None:
+            if mask.ndim == 3:
+                mask = mask.unsqueeze(1)
+            if mask.ndim == 4:
+                mask = mask.unsqueeze(2)
+            mask = 1.0 - mask
+
+        model_patched.set_model_double_block_patch(DiffSynthCnetPatch(model_patch, vae, image, strength, mask))
         return (model_patched,)