[WIP] SD3.5 IP-Adapter Pipeline Integration (huggingface#9987)

* Added support for single IPAdapter on SD3.5 pipeline



---------

Co-authored-by: hlky <[email protected]>
Co-authored-by: Steven Liu <[email protected]>
Co-authored-by: YiYi Xu <[email protected]>

docs/source/en/_toctree.yml

@@ -238,6 +238,8 @@
       title: Textual Inversion
     - local: api/loaders/unet
       title: UNet
+    - local: api/loaders/transformer_sd3
+      title: SD3Transformer2D
     - local: api/loaders/peft
       title: PEFT
     title: Loaders

docs/source/en/api/attnprocessor.md

@@ -86,6 +86,8 @@ An attention processor is a class for applying different types of attention mech
 
 [[autodoc]] models.attention_processor.IPAdapterAttnProcessor2_0
 
+[[autodoc]] models.attention_processor.SD3IPAdapterJointAttnProcessor2_0
+
 ## JointAttnProcessor2_0
 
 [[autodoc]] models.attention_processor.JointAttnProcessor2_0

docs/source/en/api/loaders/ip_adapter.md

@@ -24,6 +24,12 @@ Learn how to load an IP-Adapter checkpoint and image in the IP-Adapter [loading]
 
 [[autodoc]] loaders.ip_adapter.IPAdapterMixin
 
+## SD3IPAdapterMixin
+
+[[autodoc]] loaders.ip_adapter.SD3IPAdapterMixin
+    - all
+    - is_ip_adapter_active
+
 ## IPAdapterMaskProcessor
 
 [[autodoc]] image_processor.IPAdapterMaskProcessor

docs/source/en/api/loaders/transformer_sd3.md

@@ -0,0 +1,29 @@
+<!--Copyright 2024 The HuggingFace Team. All rights reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
+the License. You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
+an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
+specific language governing permissions and limitations under the License.
+-->
+
+# SD3Transformer2D
+
+This class is useful when *only* loading weights into a [`SD3Transformer2DModel`]. If you need to load weights into the text encoder or a text encoder and SD3Transformer2DModel, check [`SD3LoraLoaderMixin`](lora#diffusers.loaders.SD3LoraLoaderMixin) class instead.
+
+The [`SD3Transformer2DLoadersMixin`] class currently only loads IP-Adapter weights, but will be used in the future to save weights and load LoRAs.
+
+<Tip>
+
+To learn more about how to load LoRA weights, see the [LoRA](../../using-diffusers/loading_adapters#lora) loading guide.
+
+</Tip>
+
+## SD3Transformer2DLoadersMixin
+
+[[autodoc]] loaders.transformer_sd3.SD3Transformer2DLoadersMixin
+    - all
+    - _load_ip_adapter_weights

docs/source/en/api/pipelines/stable_diffusion/stable_diffusion_3.md

@@ -59,9 +59,76 @@ image.save("sd3_hello_world.png")
 - [`stabilityai/stable-diffusion-3.5-large`](https://huggingface.co/stabilityai/stable-diffusion-3-5-large)
 - [`stabilityai/stable-diffusion-3.5-large-turbo`](https://huggingface.co/stabilityai/stable-diffusion-3-5-large-turbo)
 
+## Image Prompting with IP-Adapters
+
+An IP-Adapter lets you prompt SD3 with images, in addition to the text prompt. This is especially useful when describing complex concepts that are difficult to articulate through text alone and you have reference images. To load and use an IP-Adapter, you need:
+
+- `image_encoder`: Pre-trained vision model used to obtain image features, usually a CLIP image encoder.
+- `feature_extractor`: Image processor that prepares the input image for the chosen `image_encoder`.
+- `ip_adapter_id`: Checkpoint containing parameters of image cross attention layers and image projection. 
+
+IP-Adapters are trained for a specific model architecture, so they also work in finetuned variations of the base model. You can use the [`~SD3IPAdapterMixin.set_ip_adapter_scale`] function to adjust how strongly the output aligns with the image prompt. The higher the value, the more closely the model follows the image prompt. A default value of 0.5 is typically a good balance, ensuring the model considers both the text and image prompts equally.
+
+```python
+import torch
+from PIL import Image
+
+from diffusers import StableDiffusion3Pipeline
+from transformers import SiglipVisionModel, SiglipImageProcessor
+
+image_encoder_id = "google/siglip-so400m-patch14-384"
+ip_adapter_id = "InstantX/SD3.5-Large-IP-Adapter"
+
+feature_extractor = SiglipImageProcessor.from_pretrained(
+    image_encoder_id,
+    torch_dtype=torch.float16
+)
+image_encoder = SiglipVisionModel.from_pretrained(
+    image_encoder_id,
+    torch_dtype=torch.float16
+).to( "cuda")
+
+pipe = StableDiffusion3Pipeline.from_pretrained(
+    "stabilityai/stable-diffusion-3.5-large",
+    torch_dtype=torch.float16,
+    feature_extractor=feature_extractor,
+    image_encoder=image_encoder,
+).to("cuda")
+
+pipe.load_ip_adapter(ip_adapter_id)
+pipe.set_ip_adapter_scale(0.6)
+
+ref_img = Image.open("image.jpg").convert('RGB')
+
+image = pipe(
+    width=1024,
+    height=1024,
+    prompt="a cat",
+    negative_prompt="lowres, low quality, worst quality",
+    num_inference_steps=24,
+    guidance_scale=5.0,
+    ip_adapter_image=ref_img
+).images[0]
+
+image.save("result.jpg")
+```
+
+<div class="justify-center">
+    <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/sd3_ip_adapter_example.png"/>
+    <figcaption class="mt-2 text-sm text-center text-gray-500">IP-Adapter examples with prompt "a cat"</figcaption>
+</div>
+
+
+<Tip>
+
+Check out [IP-Adapter](../../../using-diffusers/ip_adapter) to learn more about how IP-Adapters work.
+
+</Tip>
+
+
 ## Memory Optimisations for SD3
 
-SD3 uses three text encoders, one if which is the very large T5-XXL model. This makes it challenging to run the model on GPUs with less than 24GB of VRAM, even when using `fp16` precision. The following section outlines a few memory optimizations in Diffusers that make it easier to run SD3 on low resource hardware.
+SD3 uses three text encoders, one of which is the very large T5-XXL model. This makes it challenging to run the model on GPUs with less than 24GB of VRAM, even when using `fp16` precision. The following section outlines a few memory optimizations in Diffusers that make it easier to run SD3 on low resource hardware.
 
 ### Running Inference with Model Offloading
 

src/diffusers/loaders/__init__.py

@@ -56,6 +56,7 @@ def text_encoder_attn_modules(text_encoder):
 if is_torch_available():
     _import_structure["single_file_model"] = ["FromOriginalModelMixin"]
 
+    _import_structure["transformer_sd3"] = ["SD3Transformer2DLoadersMixin"]
     _import_structure["unet"] = ["UNet2DConditionLoadersMixin"]
     _import_structure["utils"] = ["AttnProcsLayers"]
     if is_transformers_available():
@@ -74,19 +75,26 @@ def text_encoder_attn_modules(text_encoder):
             "SanaLoraLoaderMixin",
         ]
         _import_structure["textual_inversion"] = ["TextualInversionLoaderMixin"]
-        _import_structure["ip_adapter"] = ["IPAdapterMixin"]
+        _import_structure["ip_adapter"] = [
+            "IPAdapterMixin",
+            "SD3IPAdapterMixin",
+        ]
 
 _import_structure["peft"] = ["PeftAdapterMixin"]
 
 
 if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
     if is_torch_available():
         from .single_file_model import FromOriginalModelMixin
+        from .transformer_sd3 import SD3Transformer2DLoadersMixin
         from .unet import UNet2DConditionLoadersMixin
         from .utils import AttnProcsLayers
 
         if is_transformers_available():
-            from .ip_adapter import IPAdapterMixin
+            from .ip_adapter import (
+                IPAdapterMixin,
+                SD3IPAdapterMixin,
+            )
             from .lora_pipeline import (
                 AmusedLoraLoaderMixin,
                 CogVideoXLoraLoaderMixin,