fix offloading for sd3.5 controlnets

src/diffusers/models/controlnets/controlnet_sd3.py

@@ -266,6 +266,20 @@ def _set_gradient_checkpointing(self, module, value=False):
         if hasattr(module, "gradient_checkpointing"):
             module.gradient_checkpointing = value
 
+    # Notes: This is for SD3.5 8b controlnet, which shares the pos_embed with the transformer
+    # we should have handled this in conversion script
+    def _get_pos_embed_from_transformer(self, transformer):
+        pos_embed = PatchEmbed(
+            height=transformer.config.sample_size,
+            width=transformer.config.sample_size,
+            patch_size=transformer.config.patch_size,
+            in_channels=transformer.config.in_channels,
+            embed_dim=transformer.inner_dim,
+            pos_embed_max_size=transformer.config.pos_embed_max_size,
+        )
+        pos_embed.load_state_dict(transformer.pos_embed.state_dict(), strict=True)
+        return pos_embed
+
     @classmethod
     def from_transformer(
         cls, transformer, num_layers=12, num_extra_conditioning_channels=1, load_weights_from_transformer=True

src/diffusers/pipelines/controlnet_sd3/pipeline_stable_diffusion_3_controlnet.py

@@ -193,7 +193,19 @@ def __init__(
     ):
         super().__init__()
         if isinstance(controlnet, (list, tuple)):
+            for controlnet_model in controlnet:
+                # for SD3.5 8b controlnet, it shares the pos_embed with the transformer
+                if (
+                    hasattr(controlnet_model.config, "use_pos_embed")
+                    and controlnet_model.config.use_pos_embed is False
+                ):
+                    pos_embed = controlnet_model._get_pos_embed_from_transformer(transformer)
+                    controlnet_model.pos_embed = pos_embed.to(controlnet_model.dtype).to(controlnet_model.device)
             controlnet = SD3MultiControlNetModel(controlnet)
+        else:
+            if hasattr(controlnet.config, "use_pos_embed") and controlnet.config.use_pos_embed is False:
+                pos_embed = controlnet._get_pos_embed_from_transformer(transformer)
+                controlnet.pos_embed = pos_embed.to(controlnet.dtype).to(controlnet.device)
 
         self.register_modules(
             vae=vae,
@@ -1042,15 +1054,9 @@ def __call__(
                         controlnet_cond_scale = controlnet_cond_scale[0]
                     cond_scale = controlnet_cond_scale * controlnet_keep[i]
 
-                if controlnet_config.use_pos_embed is False:
-                    # sd35 (offical) 8b controlnet
-                    controlnet_model_input = self.transformer.pos_embed(latent_model_input)
-                else:
-                    controlnet_model_input = latent_model_input
-
                 # controlnet(s) inference
                 control_block_samples = self.controlnet(
-                    hidden_states=controlnet_model_input,
+                    hidden_states=latent_model_input,
                     timestep=timestep,
                     encoder_hidden_states=controlnet_encoder_hidden_states,
                     pooled_projections=controlnet_pooled_projections,