Classifier free guidance unconditioned value

monai/inferers/inferer.py

@@ -916,6 +916,7 @@ def sample(
         verbose: bool = True,
         seg: torch.Tensor | None = None,
         cfg: float | None = None,
+        cfg_fill_value: float = -1.0,
     ) -> torch.Tensor | tuple[torch.Tensor, list[torch.Tensor]]:
         """
         Args:
@@ -929,6 +930,7 @@ def sample(
             verbose: if true, prints the progression bar of the sampling process.
             seg: if diffusion model is instance of SPADEDiffusionModel, segmentation must be provided.
             cfg: classifier-free-guidance scale, which indicates the level of strengthening on the conditioning.
+            cfg_fill_value: the fill value to use for the unconditioned input when using classifier-free guidance.
         """
         if mode not in ["crossattn", "concat"]:
             raise NotImplementedError(f"{mode} condition is not supported")
@@ -961,7 +963,7 @@ def sample(
                 model_input = torch.cat([image] * 2, dim=0)
                 if conditioning is not None:
                     uncondition = torch.ones_like(conditioning)
-                    uncondition.fill_(-1)
+                    uncondition.fill_(cfg_fill_value)
                     conditioning_input = torch.cat([uncondition, conditioning], dim=0)
                 else:
                     conditioning_input = None
@@ -1261,6 +1263,7 @@ def sample(  # type: ignore[override]
         verbose: bool = True,
         seg: torch.Tensor | None = None,
         cfg: float | None = None,
+        cfg_fill_value: float = -1.0,
     ) -> torch.Tensor | tuple[torch.Tensor, list[torch.Tensor]]:
         """
         Args:
@@ -1276,6 +1279,7 @@ def sample(  # type: ignore[override]
             seg: if diffusion model is instance of SPADEDiffusionModel, or autoencoder_model
              is instance of SPADEAutoencoderKL, segmentation must be provided.
             cfg: classifier-free-guidance scale, which indicates the level of strengthening on the conditioning.
+            cfg_fill_value: the fill value to use for the unconditioned input when using classifier-free guidance.
         """
 
         if (
@@ -1300,6 +1304,7 @@ def sample(  # type: ignore[override]
             verbose=verbose,
             seg=seg,
             cfg=cfg,
+            cfg_fill_value=cfg_fill_value,
         )
 
         if save_intermediates:
@@ -1479,6 +1484,7 @@ def sample(  # type: ignore[override]
         verbose: bool = True,
         seg: torch.Tensor | None = None,
         cfg: float | None = None,
+        cfg_fill_value: float = -1.0,
     ) -> torch.Tensor | tuple[torch.Tensor, list[torch.Tensor]]:
         """
         Args:
@@ -1493,7 +1499,8 @@ def sample(  # type: ignore[override]
             mode: Conditioning mode for the network.
             verbose: if true, prints the progression bar of the sampling process.
             seg: if diffusion model is instance of SPADEDiffusionModel, segmentation must be provided.
-                        cfg: classifier-free-guidance scale, which indicates the level of strengthening on the conditioning.
+            cfg: classifier-free-guidance scale, which indicates the level of strengthening on the conditioning.
+            cfg_fill_value: the fill value to use for the unconditioned input when using classifier-free guidance.
         """
         if mode not in ["crossattn", "concat"]:
             raise NotImplementedError(f"{mode} condition is not supported")
@@ -1521,7 +1528,7 @@ def sample(  # type: ignore[override]
                 model_input = torch.cat([image] * 2, dim=0)
                 if conditioning is not None:
                     uncondition = torch.ones_like(conditioning)
-                    uncondition.fill_(-1)
+                    uncondition.fill_(cfg_fill_value)
                     conditioning_input = torch.cat([uncondition, conditioning], dim=0)
                 else:
                     conditioning_input = None
@@ -1839,6 +1846,7 @@ def sample(  # type: ignore[override]
         verbose: bool = True,
         seg: torch.Tensor | None = None,
         cfg: float | None = None,
+        cfg_fill_value: float = -1.0,
     ) -> torch.Tensor | tuple[torch.Tensor, list[torch.Tensor]]:
         """
         Args:
@@ -1856,6 +1864,7 @@ def sample(  # type: ignore[override]
             seg: if diffusion model is instance of SPADEDiffusionModel, or autoencoder_model
              is instance of SPADEAutoencoderKL, segmentation must be provided.
             cfg: classifier-free-guidance scale, which indicates the level of strengthening on the conditioning.
+            cfg_fill_value: the fill value to use for the unconditioned input when using classifier-free guidance.
         """
 
         if (
@@ -1884,6 +1893,7 @@ def sample(  # type: ignore[override]
             verbose=verbose,
             seg=seg,
             cfg=cfg,
+            cfg_fill_value=cfg_fill_value,
         )
 
         if save_intermediates:

tests/inferers/test_diffusion_inferer.py

@@ -106,6 +106,7 @@ def test_sample_cfg(self, model_params, input_shape):
             save_intermediates=True,
             intermediate_steps=1,
             cfg=5,
+            cfg_fill_value=-1,
         )
         self.assertEqual(sample.shape, noise.shape)
 

tests/inferers/test_latent_diffusion_inferer.py

@@ -456,6 +456,7 @@ def test_sample_shape_with_cfg(
                     scheduler=scheduler,
                     seg=input_seg,
                     cfg=5,
+                    cfg_fill_value=-1,
                 )
             else:
                 sample = inferer.sample(