offload the optional module image_encoder

src/diffusers/pipelines/animatediff/pipeline_animatediff.py

@@ -106,7 +106,7 @@ class AnimateDiffPipeline(DiffusionPipeline, TextualInversionLoaderMixin, IPAdap
             [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
     """
 
-    model_cpu_offload_seq = "text_encoder->unet->vae"
+    model_cpu_offload_seq = "text_encoder->image_encoder->unet->vae"
     _optional_components = ["feature_extractor", "image_encoder"]
 
     def __init__(

src/diffusers/pipelines/controlnet/pipeline_controlnet.py

@@ -176,7 +176,7 @@ class StableDiffusionControlNetPipeline(
             A `CLIPImageProcessor` to extract features from generated images; used as inputs to the `safety_checker`.
     """
 
-    model_cpu_offload_seq = "text_encoder->unet->vae"
+    model_cpu_offload_seq = "text_encoder->image_encoder->unet->vae"
     _optional_components = ["safety_checker", "feature_extractor", "image_encoder"]
     _exclude_from_cpu_offload = ["safety_checker"]
     _callback_tensor_inputs = ["latents", "prompt_embeds", "negative_prompt_embeds"]

src/diffusers/pipelines/controlnet/pipeline_controlnet_inpaint.py

@@ -291,7 +291,7 @@ class StableDiffusionControlNetInpaintPipeline(
             A `CLIPImageProcessor` to extract features from generated images; used as inputs to the `safety_checker`.
     """
 
-    model_cpu_offload_seq = "text_encoder->unet->vae"
+    model_cpu_offload_seq = "text_encoder->image_encoder->unet->vae"
     _optional_components = ["safety_checker", "feature_extractor", "image_encoder"]
     _exclude_from_cpu_offload = ["safety_checker"]
     _callback_tensor_inputs = ["latents", "prompt_embeds", "negative_prompt_embeds"]

src/diffusers/pipelines/controlnet/pipeline_controlnet_sd_xl.py

@@ -165,7 +165,7 @@ class StableDiffusionXLControlNetPipeline(
     """
 
     # leave controlnet out on purpose because it iterates with unet
-    model_cpu_offload_seq = "text_encoder->text_encoder_2->unet->vae"
+    model_cpu_offload_seq = "text_encoder->text_encoder_2->image_encoder->unet->vae"
     _optional_components = [
         "tokenizer",
         "tokenizer_2",

src/diffusers/pipelines/deprecated/alt_diffusion/pipeline_alt_diffusion.py

@@ -155,7 +155,7 @@ class AltDiffusionPipeline(
             A `CLIPImageProcessor` to extract features from generated images; used as inputs to the `safety_checker`.
     """
 
-    model_cpu_offload_seq = "text_encoder->unet->vae"
+    model_cpu_offload_seq = "text_encoder->image_encoder->unet->vae"
     _optional_components = ["safety_checker", "feature_extractor", "image_encoder"]
     _exclude_from_cpu_offload = ["safety_checker"]
     _callback_tensor_inputs = ["latents", "prompt_embeds", "negative_prompt_embeds"]

src/diffusers/pipelines/deprecated/alt_diffusion/pipeline_alt_diffusion_img2img.py

@@ -195,7 +195,7 @@ class AltDiffusionImg2ImgPipeline(
             A `CLIPImageProcessor` to extract features from generated images; used as inputs to the `safety_checker`.
     """
 
-    model_cpu_offload_seq = "text_encoder->unet->vae"
+    model_cpu_offload_seq = "text_encoder->image_encoder->unet->vae"
     _optional_components = ["safety_checker", "feature_extractor", "image_encoder"]
     _exclude_from_cpu_offload = ["safety_checker"]
     _callback_tensor_inputs = ["latents", "prompt_embeds", "negative_prompt_embeds"]

src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion.py

@@ -151,7 +151,7 @@ class StableDiffusionPipeline(
             A `CLIPImageProcessor` to extract features from generated images; used as inputs to the `safety_checker`.
     """
 
-    model_cpu_offload_seq = "text_encoder->unet->vae"
+    model_cpu_offload_seq = "text_encoder->image_encoder->unet->vae"
     _optional_components = ["safety_checker", "feature_extractor", "image_encoder"]
     _exclude_from_cpu_offload = ["safety_checker"]
     _callback_tensor_inputs = ["latents", "prompt_embeds", "negative_prompt_embeds"]

src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_img2img.py

@@ -191,7 +191,7 @@ class StableDiffusionImg2ImgPipeline(
             A `CLIPImageProcessor` to extract features from generated images; used as inputs to the `safety_checker`.
     """
 
-    model_cpu_offload_seq = "text_encoder->unet->vae"
+    model_cpu_offload_seq = "text_encoder->image_encoder->unet->vae"
     _optional_components = ["safety_checker", "feature_extractor", "image_encoder"]
     _exclude_from_cpu_offload = ["safety_checker"]
     _callback_tensor_inputs = ["latents", "prompt_embeds", "negative_prompt_embeds"]

src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_inpaint.py

@@ -255,7 +255,7 @@ class StableDiffusionInpaintPipeline(
             A `CLIPImageProcessor` to extract features from generated images; used as inputs to the `safety_checker`.
     """
 
-    model_cpu_offload_seq = "text_encoder->unet->vae"
+    model_cpu_offload_seq = "text_encoder->image_encoder->unet->vae"
     _optional_components = ["safety_checker", "feature_extractor", "image_encoder"]
     _exclude_from_cpu_offload = ["safety_checker"]
     _callback_tensor_inputs = ["latents", "prompt_embeds", "negative_prompt_embeds", "mask", "masked_image_latents"]

src/diffusers/pipelines/stable_diffusion_xl/pipeline_stable_diffusion_xl.py

@@ -198,7 +198,7 @@ class StableDiffusionXLPipeline(
             watermarker will be used.
     """
 
-    model_cpu_offload_seq = "text_encoder->text_encoder_2->unet->vae"
+    model_cpu_offload_seq = "text_encoder->text_encoder_2->image_encoder->unet->vae"
     _optional_components = [
         "tokenizer",
         "tokenizer_2",

src/diffusers/pipelines/stable_diffusion_xl/pipeline_stable_diffusion_xl_img2img.py

@@ -219,7 +219,7 @@ class StableDiffusionXLImg2ImgPipeline(
             watermarker will be used.
     """
 
-    model_cpu_offload_seq = "text_encoder->text_encoder_2->unet->vae"
+    model_cpu_offload_seq = "text_encoder->text_encoder_2->image_encoder->unet->vae"
     _optional_components = [
         "tokenizer",
         "tokenizer_2",

src/diffusers/pipelines/stable_diffusion_xl/pipeline_stable_diffusion_xl_inpaint.py

@@ -364,7 +364,7 @@ class StableDiffusionXLInpaintPipeline(
             watermarker will be used.
     """
 
-    model_cpu_offload_seq = "text_encoder->text_encoder_2->unet->vae"
+    model_cpu_offload_seq = "text_encoder->text_encoder_2->image_encoder->unet->vae"
 
     _optional_components = [
         "tokenizer",

tests/pipelines/ip_adapters/test_ip_adapter_stable_diffusion.py

@@ -182,6 +182,33 @@ def test_inpainting(self):
 
         assert np.allclose(image_slice, expected_slice, atol=1e-4, rtol=1e-4)
 
+    def test_text_to_image_model_cpu_offload(self):
+        image_encoder = self.get_image_encoder(repo_id="h94/IP-Adapter", subfolder="models/image_encoder")
+        pipeline = StableDiffusionPipeline.from_pretrained(
+            "runwayml/stable-diffusion-v1-5", image_encoder=image_encoder, safety_checker=None, torch_dtype=self.dtype
+        )
+        pipeline.load_ip_adapter("h94/IP-Adapter", subfolder="models", weight_name="ip-adapter_sd15.bin")
+        pipeline.to(torch_device)
+
+        inputs = self.get_dummy_inputs()
+        output_without_offload = pipeline(**inputs).images
+
+        pipeline.enable_model_cpu_offload()
+        inputs = self.get_dummy_inputs()
+        output_with_offload = pipeline(**inputs).images
+        max_diff = np.abs(output_with_offload - output_without_offload).max()
+        self.assertLess(max_diff, 1e-3, "CPU offloading should not affect the inference results")
+
+        offloaded_modules = [
+            v
+            for k, v in pipeline.components.items()
+            if isinstance(v, torch.nn.Module) and k not in pipeline._exclude_from_cpu_offload
+        ]
+        (
+            self.assertTrue(all(v.device.type == "cpu" for v in offloaded_modules)),
+            f"Not offloaded: {[v for v in offloaded_modules if v.device.type != 'cpu']}",
+        )
+
     def test_text_to_image_full_face(self):
         image_encoder = self.get_image_encoder(repo_id="h94/IP-Adapter", subfolder="models/image_encoder")
         pipeline = StableDiffusionPipeline.from_pretrained(