Add a CONDConstant for passing non tensor conds to unet.

comfy/conds.py

@@ -62,3 +62,18 @@ class CONDCrossAttn(CONDRegular):
                 c = c.repeat(1, crossattn_max_len // c.shape[1], 1) #padding with repeat doesn't change result
             out.append(c)
         return torch.cat(out)
+
+class CONDConstant(CONDRegular):
+    def __init__(self, cond):
+        self.cond = cond
+
+    def process_cond(self, batch_size, device, **kwargs):
+        return self._copy_with(self.cond)
+
+    def can_concat(self, other):
+        if self.cond != other.cond:
+            return False
+        return True
+
+    def concat(self, others):
+        return self.cond

comfy/model_base.py

@@ -61,7 +61,10 @@ class BaseModel(torch.nn.Module):
         context = context.to(dtype)
         extra_conds = {}
         for o in kwargs:
-            extra_conds[o] = kwargs[o].to(dtype)
+            extra = kwargs[o]
+            if hasattr(extra, "to"):
+                extra = extra.to(dtype)
+            extra_conds[o] = extra
         model_output = self.diffusion_model(xc, t, context=context, control=control, transformer_options=transformer_options, **extra_conds).float()
         return self.model_sampling.calculate_denoised(sigma, model_output, x)