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[docs] IP-Adapter (#6897) 

* use cases

* first draft

* fix image links

* lcm-lora

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docs/source/en/_toctree.yml
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...@@ -58,6 +58,8 @@ ...@@ -58,6 +58,8 @@
- sections: - sections:
- local: using-diffusers/textual_inversion_inference - local: using-diffusers/textual_inversion_inference
title: Textual inversion title: Textual inversion
- local: using-diffusers/ip_adapter
title: IP-Adapter
- local: training/distributed_inference - local: training/distributed_inference
title: Distributed inference with multiple GPUs title: Distributed inference with multiple GPUs
- local: using-diffusers/reusing_seeds - local: using-diffusers/reusing_seeds
......
docs/source/en/api/loaders/ip_adapter.md
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...@@ -12,11 +12,11 @@ specific language governing permissions and limitations under the License. ...@@ -12,11 +12,11 @@ specific language governing permissions and limitations under the License.
# IP-Adapter # IP-Adapter
[IP-Adapter](https://hf.co/papers/2308.06721) is a lightweight adapter that enables prompting a diffusion model with an image. This method decouples the cross-attention layers of the image and text features. The image features are generated from an image encoder. Files generated from IP-Adapter are only ~100MBs. [IP-Adapter](https://hf.co/papers/2308.06721) is a lightweight adapter that enables prompting a diffusion model with an image. This method decouples the cross-attention layers of the image and text features. The image features are generated from an image encoder.
<Tip> <Tip>
Learn how to load an IP-Adapter checkpoint and image in the [IP-Adapter](../../using-diffusers/loading_adapters#ip-adapter) loading guide. Learn how to load an IP-Adapter checkpoint and image in the IP-Adapter [loading](../../using-diffusers/loading_adapters#ip-adapter) guide, and you can see how to use it in the [usage](../../using-diffusers/ip_adapter) guide.
</Tip> </Tip>
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docs/source/en/using-diffusers/ip_adapter.md 0 → 100644
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# IP-Adapter
[IP-Adapter](https://hf.co/papers/2308.06721) is an image prompt adapter that can be plugged into diffusion models to enable image prompting without any changes to the underlying model. Furthermore, this adapter can be reused with other models finetuned from the same base model and it can be combined with other adapters like [ControlNet](../using-diffusers/controlnet). The key idea behind IP-Adapter is the *decoupled cross-attention* mechanism which adds a separate cross-attention layer just for image features instead of using the same cross-attention layer for both text and image features. This allows the model to learn more image-specific features.
> [!TIP]
> Learn how to load an IP-Adapter in the [Load adapters](../using-diffusers/loading_adapters#ip-adapter) guide, and make sure you check out the [IP-Adapter Plus](../using-diffusers/loading_adapters#ip-adapter-plus) section which requires manually loading the image encoder.
This guide will walk you through using IP-Adapter for various tasks and use cases.
## General tasks
Let's take a look at how to use IP-Adapter's image prompting capabilities with the [`StableDiffusionXLPipeline`] for tasks like text-to-image, image-to-image, and inpainting. We also encourage you to try out other pipelines such as Stable Diffusion, LCM-LoRA, ControlNet, T2I-Adapter, or AnimateDiff!
In all the following examples, you'll see the [`~loaders.IPAdapterMixin.set_ip_adapter_scale`] method. This method controls the amount of text or image conditioning to apply to the model. A value of `1.0` means the model is only conditioned on the image prompt. Lowering this value encourages the model to produce more diverse images, but they may not be as aligned with the image prompt. Typically, a value of `0.5` achieves a good balance between the two prompt types and produces good results.
<hfoptions id="tasks">
<hfoption id="Text-to-image">
Crafting the precise text prompt to generate the image you want can be difficult because it may not always capture what you'd like to express. Adding an image alongside the text prompt helps the model better understand what it should generate and can lead to more accurate results.
Load a Stable Diffusion XL (SDXL) model and insert an IP-Adapter into the model with the [`~loaders.IPAdapterMixin.load_ip_adapter`] method. Use the `subfolder` parameter to load the SDXL model weights.
```py
from diffusers import AutoPipelineForText2Image
from diffusers.utils import load_image
import torch
pipeline = AutoPipelineForText2Image.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16).to("cuda")
pipeline.load_ip_adapter("h94/IP-Adapter", subfolder="sdxl_models", weight_name="ip-adapter_sdxl.bin")
pipeline.set_ip_adapter_scale(0.6)
```
Create a text prompt and load an image prompt before passing them to the pipeline to generate an image.
```py
image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/ip_adapter_diner.png")
generator = torch.Generator(device="cpu").manual_seed(0)
images = pipeline(
prompt="a polar bear sitting in a chair drinking a milkshake",
ip_adapter_image=image,
negative_prompt="deformed, ugly, wrong proportion, low res, bad anatomy, worst quality, low quality",
num_inference_steps=100,
generator=generator,
).images
images[0]
```
<div class="flex flex-row gap-4">
<div class="flex-1">
<img class="rounded-xl" src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/ip_adapter_diner.png"/>
<figcaption class="mt-2 text-center text-sm text-gray-500">IP-Adapter image</figcaption>
</div>
<div class="flex-1">
<img class="rounded-xl" src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/ip_adapter_diner_2.png"/>
<figcaption class="mt-2 text-center text-sm text-gray-500">generated image</figcaption>
</div>
</div>
</hfoption>
<hfoption id="Image-to-image">
IP-Adapter can also help with image-to-image by guiding the model to generate an image that resembles the original image and the image prompt.
Load a Stable Diffusion XL (SDXL) model and insert an IP-Adapter into the model with the [`~loaders.IPAdapterMixin.load_ip_adapter`] method. Use the `subfolder` parameter to load the SDXL model weights.
```py
from diffusers import AutoPipelineForImage2Image
from diffusers.utils import load_image
import torch
pipeline = AutoPipelineForImage2Image.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16).to("cuda")
pipeline.load_ip_adapter("h94/IP-Adapter", subfolder="sdxl_models", weight_name="ip-adapter_sdxl.bin")
pipeline.set_ip_adapter_scale(0.6)
```
Pass the original image and the IP-Adapter image prompt to the pipeline to generate an image. Providing a text prompt to the pipeline is optional, but in this example, a text prompt is used to increase image quality.
```py
image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/ip_adapter_bear_1.png")
ip_image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/ip_adapter_bear_2.png")
generator = torch.Generator(device="cpu").manual_seed(4)
images = pipeline(
prompt="best quality, high quality",
image=image,
ip_adapter_image=ip_image,
generator=generator,
strength=0.6,
).images
images[0]
```
<div class="flex gap-4">
<div>
<img class="rounded-xl" src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/ip_adapter_bear_1.png"/>
<figcaption class="mt-2 text-center text-sm text-gray-500">original image</figcaption>
</div>
<div>
<img class="rounded-xl" src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/ip_adapter_bear_2.png"/>
<figcaption class="mt-2 text-center text-sm text-gray-500">IP-Adapter image</figcaption>
</div>
<div>
<img class="rounded-xl" src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/ip_adapter_bear_3.png"/>
<figcaption class="mt-2 text-center text-sm text-gray-500">generated image</figcaption>
</div>
</div>
</hfoption>
<hfoption id="Inpainting">
IP-Adapter is also useful for inpainting because the image prompt allows you to be much more specific about what you'd like to generate.
Load a Stable Diffusion XL (SDXL) model and insert an IP-Adapter into the model with the [`~loaders.IPAdapterMixin.load_ip_adapter`] method. Use the `subfolder` parameter to load the SDXL model weights.
```py
from diffusers import AutoPipelineForInpainting
from diffusers.utils import load_image
import torch
pipeline = AutoPipelineForInpainting.from_pretrained("diffusers/stable-diffusion-xl-1.0-inpainting-0.1", torch_dtype=torch.float16).to("cuda")
pipeline.load_ip_adapter("h94/IP-Adapter", subfolder="sdxl_models", weight_name="ip-adapter_sdxl.bin")
pipeline.set_ip_adapter_scale(0.6)
```
Pass a prompt, the original image, mask image, and the IP-Adapter image prompt to the pipeline to generate an image.
```py
mask_image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/ip_adapter_mask.png")
image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/ip_adapter_bear_1.png")
ip_image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/ip_adapter_gummy.png")
generator = torch.Generator(device="cpu").manual_seed(4)
images = pipeline(
prompt="a cute gummy bear waving",
image=image,
mask_image=mask_image,
ip_adapter_image=ip_image,
generator=generator,
num_inference_steps=100,
).images
images[0]
```
<div class="flex gap-4">
<div>
<img class="rounded-xl" src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/ip_adapter_bear_1.png"/>
<figcaption class="mt-2 text-center text-sm text-gray-500">original image</figcaption>
</div>
<div>
<img class="rounded-xl" src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/ip_adapter_gummy.png"/>
<figcaption class="mt-2 text-center text-sm text-gray-500">IP-Adapter image</figcaption>
</div>
<div>
<img class="rounded-xl" src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/ip_adapter_inpaint.png"/>
<figcaption class="mt-2 text-center text-sm text-gray-500">generated image</figcaption>
</div>
</div>
</hfoption>
<hfoption id="Video">
IP-Adapter can also help you generate videos that are more aligned with your text prompt. For example, let's load [AnimateDiff](../api/pipelines/animatediff) with its motion adapter and insert an IP-Adapter into the model with the [`~loaders.IPAdapterMixin.load_ip_adapter`] method.
> [!WARNING]
> If you're planning on offloading the model to the CPU, make sure you run it after you've loaded the IP-Adapter. When you call [`~DiffusionPipeline.enable_model_cpu_offload`] before loading the IP-Adapter, it offloads the image encoder module to the CPU and it'll return an error when you try to run the pipeline.
```py
import torch
from diffusers import AnimateDiffPipeline, DDIMScheduler, MotionAdapter
from diffusers.utils import export_to_gif
from diffusers.utils import load_image
adapter = MotionAdapter.from_pretrained("guoyww/animatediff-motion-adapter-v1-5-2", torch_dtype=torch.float16)
pipeline = AnimateDiffPipeline.from_pretrained("emilianJR/epiCRealism", motion_adapter=adapter, torch_dtype=torch.float16)
scheduler = DDIMScheduler.from_pretrained(
"emilianJR/epiCRealism",
subfolder="scheduler",
clip_sample=False,
timestep_spacing="linspace",
beta_schedule="linear",
steps_offset=1,
)
pipeline.scheduler = scheduler
pipeline.enable_vae_slicing()
pipeline.load_ip_adapter("h94/IP-Adapter", subfolder="models", weight_name="ip-adapter_sd15.bin")
pipeline.enable_model_cpu_offload()
```
Pass a prompt and an image prompt to the pipeline to generate a short video.
```py
ip_adapter_image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/ip_adapter_inpaint.png")
output = pipeline(
prompt="A cute gummy bear waving",
negative_prompt="bad quality, worse quality, low resolution",
ip_adapter_image=ip_adapter_image,
num_frames=16,
guidance_scale=7.5,
num_inference_steps=50,
generator=torch.Generator(device="cpu").manual_seed(0),
)
frames = output.frames[0]
export_to_gif(frames, "gummy_bear.gif")
```
<div class="flex flex-row gap-4">
<div class="flex-1">
<img class="rounded-xl" src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/ip_adapter_inpaint.png"/>
<figcaption class="mt-2 text-center text-sm text-gray-500">IP-Adapter image</figcaption>
</div>
<div class="flex-1">
<img class="rounded-xl" src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/gummy_bear.gif"/>
<figcaption class="mt-2 text-center text-sm text-gray-500">generated video</figcaption>
</div>
</div>
</hfoption>
</hfoptions>
## Specific use cases
IP-Adapter's image prompting and compatibility with other adapters and models makes it a versatile tool for a variety of use cases. This section covers some of the more popular applications of IP-Adapter, and we can't wait to see what you come up with!
### Face model
Generating accurate faces is challenging because they are complex and nuanced. Diffusers supports two IP-Adapter checkpoints specifically trained to generate faces:
* [ip-adapter-full-face_sd15.safetensors](https://huggingface.co/h94/IP-Adapter/blob/main/models/ip-adapter-full-face_sd15.safetensors) is conditioned with images of cropped faces and removed backgrounds
* [ip-adapter-plus-face_sd15.safetensors](https://huggingface.co/h94/IP-Adapter/blob/main/models/ip-adapter-plus-face_sd15.safetensors) uses patch embeddings and is conditioned with images of cropped faces
> [TIP]
> [IP-Adapter-FaceID](https://huggingface.co/h94/IP-Adapter-FaceID) is a face-specific IP-Adapter trained with face ID embeddings instead of CLIP image embeddings, allowing you to generate more consistent faces in different contexts and styles. Try out this popular [community pipeline](https://github.com/huggingface/diffusers/tree/main/examples/community#ip-adapter-face-id) and see how it compares to the other face IP-Adapters.
For face models, use the [h94/IP-Adapter](https://huggingface.co/h94/IP-Adapter) checkpoint. It is also recommended to use [`DDIMScheduler`] or [`EulerDiscreteScheduler`] for face models.
```py
import torch
from diffusers import StableDiffusionPipeline, DDIMScheduler
from diffusers.utils import load_image
pipeline = StableDiffusionPipeline.from_pretrained(
"runwayml/stable-diffusion-v1-5",
torch_dtype=torch.float16,
).to("cuda")
pipeline.scheduler = DDIMScheduler.from_config(pipeline.scheduler.config)
pipeline.load_ip_adapter("h94/IP-Adapter", subfolder="models", weight_name="ip-adapter-full-face_sd15.bin")
pipeline.set_ip_adapter_scale(0.5)
image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/ip_adapter_einstein_base.png")
generator = torch.Generator(device="cpu").manual_seed(26)
image = pipeline(
prompt="A photo of Einstein as a chef, wearing an apron, cooking in a French restaurant",
ip_adapter_image=image,
negative_prompt="lowres, bad anatomy, worst quality, low quality",
num_inference_steps=100,
generator=generator,
).images[0]
image
```
<div class="flex flex-row gap-4">
<div class="flex-1">
<img class="rounded-xl" src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/ip_adapter_einstein_base.png"/>
<figcaption class="mt-2 text-center text-sm text-gray-500">IP-Adapter image</figcaption>
</div>
<div class="flex-1">
<img class="rounded-xl" src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/ip_adapter_einstein.png"/>
<figcaption class="mt-2 text-center text-sm text-gray-500">generated image</figcaption>
</div>
</div>
### Multi IP-Adapter
More than one IP-Adapter can be used at the same time to generate specific images in more diverse styles. For example, you can use IP-Adapter-Face to generate consistent faces and characters, and IP-Adapter Plus to generate those faces in a specific style.
> [!TIP]
> Read the [IP-Adapter Plus](../using-diffusers/loading_adapters#ip-adapter-plus) section to learn why you need to manually load the image encoder.
Load the image encoder with [`~transformers.CLIPVisionModelWithProjection`].
```py
import torch
from diffusers import AutoPipelineForText2Image, DDIMScheduler
from transformers import CLIPVisionModelWithProjection
from diffusers.utils import load_image
image_encoder = CLIPVisionModelWithProjection.from_pretrained(
"h94/IP-Adapter",
subfolder="models/image_encoder",
torch_dtype=torch.float16,
)
```
Next, you'll load a base model, scheduler, and the IP-Adapters. The IP-Adapters to use are passed as a list to the `weight_name` parameter:
* [ip-adapter-plus_sdxl_vit-h](https://huggingface.co/h94/IP-Adapter#ip-adapter-for-sdxl-10) uses patch embeddings and a ViT-H image encoder
* [ip-adapter-plus-face_sdxl_vit-h](https://huggingface.co/h94/IP-Adapter#ip-adapter-for-sdxl-10) has the same architecture but it is conditioned with images of cropped faces
```py
pipeline = AutoPipelineForText2Image.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
torch_dtype=torch.float16,
image_encoder=image_encoder,
)
pipeline.scheduler = DDIMScheduler.from_config(pipeline.scheduler.config)
pipeline.load_ip_adapter(
"h94/IP-Adapter",
subfolder="sdxl_models",
weight_name=["ip-adapter-plus_sdxl_vit-h.safetensors", "ip-adapter-plus-face_sdxl_vit-h.safetensors"]
)
pipeline.set_ip_adapter_scale([0.7, 0.3])
pipeline.enable_model_cpu_offload()
```
Load an image prompt and a folder containing images of a certain style you want to use.
```py
face_image = load_image("https://huggingface.co/datasets/YiYiXu/testing-images/resolve/main/women_input.png")
style_folder = "https://huggingface.co/datasets/YiYiXu/testing-images/resolve/main/style_ziggy"
style_images = [load_image(f"{style_folder}/img{i}.png") for i in range(10)]
```
<div class="flex flex-row gap-4">
<div class="flex-1">
<img class="rounded-xl" src="https://huggingface.co/datasets/YiYiXu/testing-images/resolve/main/women_input.png"/>
<figcaption class="mt-2 text-center text-sm text-gray-500">IP-Adapter image of face</figcaption>
</div>
<div class="flex-1">
<img class="rounded-xl" src="https://huggingface.co/datasets/YiYiXu/testing-images/resolve/main/ip_style_grid.png"/>
<figcaption class="mt-2 text-center text-sm text-gray-500">IP-Adapter style images</figcaption>
</div>
</div>
Pass the image prompt and style images as a list to the `ip_adapter_image` parameter, and run the pipeline!
```py
generator = torch.Generator(device="cpu").manual_seed(0)
image = pipeline(
prompt="wonderwoman",
ip_adapter_image=[style_images, face_image],
negative_prompt="monochrome, lowres, bad anatomy, worst quality, low quality",
num_inference_steps=50, num_images_per_prompt=1,
generator=generator,
).images[0]
```
<div class="flex justify-center">
    <img src="https://huggingface.co/datasets/YiYiXu/testing-images/resolve/main/ip_multi_out.png" />
</div>
### Instant generation
[Latent Consistency Models (LCM)](../using-diffusers/inference_with_lcm_lora) are diffusion models that can generate images in as little as 4 steps compared to other diffusion models like SDXL that typically require way more steps. This is why image generation with an LCM feels "instantaneous". IP-Adapters can be plugged into an LCM-LoRA model to instantly generate images with an image prompt.
The IP-Adapter weights need to be loaded first, then you can use [`~StableDiffusionPipeline.load_lora_weights`] to load the LoRA style and weight you want to apply to your image.
```py
from diffusers import DiffusionPipeline, LCMScheduler
import torch
from diffusers.utils import load_image
model_id = "sd-dreambooth-library/herge-style"
lcm_lora_id = "latent-consistency/lcm-lora-sdv1-5"
pipeline = DiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16)
pipeline.load_ip_adapter("h94/IP-Adapter", subfolder="models", weight_name="ip-adapter_sd15.bin")
pipeline.load_lora_weights(lcm_lora_id)
pipeline.scheduler = LCMScheduler.from_config(pipe.scheduler.config)
pipeline.enable_model_cpu_offload()
```
Try using with a lower IP-Adapter scale to condition image generation more on the [herge_style](https://huggingface.co/sd-dreambooth-library/herge-style) checkpoint, and remember to use the special token `herge_style` in your prompt to trigger and apply the style.
```py
pipeline.set_ip_adapter_scale(0.4)
prompt = "herge_style woman in armor, best quality, high quality"
generator = torch.Generator(device="cpu").manual_seed(0)
ip_adapter_image = load_image("https://user-images.githubusercontent.com/24734142/266492875-2d50d223-8475-44f0-a7c6-08b51cb53572.png")
image = pipeline(
prompt=prompt,
ip_adapter_image=ip_adapter_image,
num_inference_steps=4,
guidance_scale=1,
).images[0]
image
```
<div class="flex justify-center">
    <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/ip_adapter_herge.png" />
</div>
### Structural control
To control image generation to an even greater degree, you can combine IP-Adapter with a model like [ControlNet](../using-diffusers/controlnet). A ControlNet is also an adapter that can be inserted into a diffusion model to allow for conditioning on an additional control image. The control image can be depth maps, edge maps, pose estimations, and more.
Load a [`ControlNetModel`] checkpoint conditioned on depth maps, insert it into a diffusion model, and load the IP-Adapter.
```py
from diffusers import StableDiffusionControlNetPipeline, ControlNetModel
import torch
from diffusers.utils import load_image
controlnet_model_path = "lllyasviel/control_v11f1p_sd15_depth"
controlnet = ControlNetModel.from_pretrained(controlnet_model_path, torch_dtype=torch.float16)
pipeline = StableDiffusionControlNetPipeline.from_pretrained(
"runwayml/stable-diffusion-v1-5", controlnet=controlnet, torch_dtype=torch.float16)
pipeline.to("cuda")
pipeline.load_ip_adapter("h94/IP-Adapter", subfolder="models", weight_name="ip-adapter_sd15.bin")
```
Now load the IP-Adapter image and depth map.
```py
ip_adapter_image = load_image("https://huggingface.co/datasets/YiYiXu/testing-images/resolve/main/statue.png")
depth_map = load_image("https://huggingface.co/datasets/YiYiXu/testing-images/resolve/main/depth.png")
```
<div class="flex flex-row gap-4">
<div class="flex-1">
<img class="rounded-xl" src="https://huggingface.co/datasets/YiYiXu/testing-images/resolve/main/statue.png"/>
<figcaption class="mt-2 text-center text-sm text-gray-500">IP-Adapter image</figcaption>
</div>
<div class="flex-1">
<img class="rounded-xl" src="https://huggingface.co/datasets/YiYiXu/testing-images/resolve/main/depth.png"/>
<figcaption class="mt-2 text-center text-sm text-gray-500">depth map</figcaption>
</div>
</div>
Pass the depth map and IP-Adapter image to the pipeline to generate an image.
```py
generator = torch.Generator(device="cpu").manual_seed(33)
image = pipeline(
prompt="best quality, high quality",
image=depth_map,
ip_adapter_image=ip_adapter_image,
negative_prompt="monochrome, lowres, bad anatomy, worst quality, low quality",
num_inference_steps=50,
generator=generator,
).image[0]
image
```
<div class="flex justify-center">
    <img src="https://huggingface.co/datasets/YiYiXu/testing-images/resolve/main/ipa-controlnet-out.png" />
</div>
docs/source/en/using-diffusers/loading_adapters.md
View file @ 9efe1e52
...@@ -308,60 +308,35 @@ image = pipeline(prompt=prompt).images[0] ...@@ -308,60 +308,35 @@ image = pipeline(prompt=prompt).images[0]
image image
``` ```
## IP-Adapter ## IP-Adapter
[IP-Adapter](https://ip-adapter.github.io/) is an effective and lightweight adapter that adds image prompting capabilities to a diffusion model. This adapter works by decoupling the cross-attention layers of the image and text features. All the other model components are frozen and only the embedded image features in the UNet are trained. As a result, IP-Adapter files are typically only ~100MBs. [IP-Adapter](https://ip-adapter.github.io/) is a lightweight adapter that enables image prompting for any diffusion model. This adapter works by decoupling the cross-attention layers of the image and text features. All the other model components are frozen and only the embedded image features in the UNet are trained. As a result, IP-Adapter files are typically only ~100MBs.
IP-Adapter works with most of our pipelines, including Stable Diffusion, Stable Diffusion XL (SDXL), ControlNet, T2I-Adapter, AnimateDiff. And you can use any custom models finetuned from the same base models. It also works with LCM-Lora out of box. You can learn more about how to use IP-Adapter for different tasks and specific use cases in the [IP-Adapter](../using-diffusers/ip_adapter) guide.
> [!TIP]
> Diffusers currently only supports IP-Adapter for some of the most popular pipelines. Feel free to open a feature request if you have a cool use case and want to integrate IP-Adapter with an unsupported pipeline!
> Official IP-Adapter checkpoints are available from [h94/IP-Adapter](https://huggingface.co/h94/IP-Adapter).
<Tip> To start, load a Stable Diffusion checkpoint.
You can find official IP-Adapter checkpoints in [h94/IP-Adapter](https://huggingface.co/h94/IP-Adapter).
IP-Adapter was contributed by [okotaku](https://github.com/okotaku).
</Tip>
Let's first create a Stable Diffusion Pipeline.
```py ```py
from diffusers import AutoPipelineForText2Image from diffusers import AutoPipelineForText2Image
import torch import torch
from diffusers.utils import load_image from diffusers.utils import load_image
pipeline = AutoPipelineForText2Image.from_pretrained("runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16).to("cuda") pipeline = AutoPipelineForText2Image.from_pretrained("runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16).to("cuda")
``` ```
Now load the [h94/IP-Adapter](https://huggingface.co/h94/IP-Adapter) weights with the [`~loaders.IPAdapterMixin.load_ip_adapter`] method. Then load the IP-Adapter weights and add it to the pipeline with the [`~loaders.IPAdapterMixin.load_ip_adapter`] method.
```py ```py
pipeline.load_ip_adapter("h94/IP-Adapter", subfolder="models", weight_name="ip-adapter_sd15.bin") pipeline.load_ip_adapter("h94/IP-Adapter", subfolder="models", weight_name="ip-adapter_sd15.bin")
``` ```
<Tip> Once loaded, you can use the pipeline with an image and text prompt to guide the image generation process.
IP-Adapter relies on an image encoder to generate the image features, if your IP-Adapter weights folder contains a "image_encoder" subfolder, the image encoder will be automatically loaded and registered to the pipeline. Otherwise you can so load a [`~transformers.CLIPVisionModelWithProjection`] model and pass it to a Stable Diffusion pipeline when you create it.
```py
from diffusers import AutoPipelineForText2Image
from transformers import CLIPVisionModelWithProjection
import torch
image_encoder = CLIPVisionModelWithProjection.from_pretrained(
"h94/IP-Adapter",
subfolder="models/image_encoder",
torch_dtype=torch.float16,
).to("cuda")
pipeline = AutoPipelineForText2Image.from_pretrained("runwayml/stable-diffusion-v1-5", image_encoder=image_encoder, torch_dtype=torch.float16).to("cuda")
```
</Tip>
IP-Adapter allows you to use both image and text to condition the image generation process. For example, let's use the bear image from the [Textual Inversion](#textual-inversion) section as the image prompt (`ip_adapter_image`) along with a text prompt to add "sunglasses". 😎
```py ```py
pipeline.set_ip_adapter_scale(0.6)
image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/load_neg_embed.png") image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/load_neg_embed.png")
generator = torch.Generator(device="cpu").manual_seed(33) generator = torch.Generator(device="cpu").manual_seed(33)
images = pipeline( images = pipeline(
...@@ -370,381 +345,32 @@ images = pipeline( ...@@ -370,381 +345,32 @@ images = pipeline(
    negative_prompt="monochrome, lowres, bad anatomy, worst quality, low quality",     negative_prompt="monochrome, lowres, bad anatomy, worst quality, low quality",
    num_inference_steps=50,     num_inference_steps=50,
    generator=generator,     generator=generator,
).images ).images[0]
images[0] images
``` ```
<div class="flex justify-center"> <div class="flex justify-center">
    <img src="https://huggingface.co/datasets/YiYiXu/testing-images/resolve/main/ip-bear.png" />     <img src="https://huggingface.co/datasets/YiYiXu/testing-images/resolve/main/ip-bear.png" />
</div> </div>
<Tip> ### IP-Adapter Plus
You can use the [`~loaders.IPAdapterMixin.set_ip_adapter_scale`] method to adjust the text prompt and image prompt condition ratio.  If you're only using the image prompt, you should set the scale to `1.0`. You can lower the scale to get more generation diversity, but it'll be less aligned with the prompt.
`scale=0.5` can achieve good results in most cases when you use both text and image prompts.
</Tip>
IP-Adapter also works great with Image-to-Image and Inpainting pipelines. See below examples of how you can use it with Image-to-Image and Inpaint.
<hfoptions id="tasks">
<hfoption id="image-to-image">
```py
from diffusers import AutoPipelineForImage2Image
import torch
from diffusers.utils import load_image
pipeline = AutoPipelineForImage2Image.from_pretrained("runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16).to("cuda")
image = load_image("https://huggingface.co/datasets/YiYiXu/testing-images/resolve/main/vermeer.jpg")
ip_image = load_image("https://huggingface.co/datasets/YiYiXu/testing-images/resolve/main/river.png")
pipeline.load_ip_adapter("h94/IP-Adapter", subfolder="models", weight_name="ip-adapter_sd15.bin") IP-Adapter relies on an image encoder to generate image features. If the IP-Adapter repository contains a `image_encoder` subfolder, the image encoder is automatically loaded and registed to the pipeline. Otherwise, you'll need to explicitly load the image encoder with a [`~transformers.CLIPVisionModelWithProjection`] model and pass it to the pipeline.
generator = torch.Generator(device="cpu").manual_seed(33)
images = pipeline(
    prompt='best quality, high quality',
    image = image,
    ip_adapter_image=ip_image,
    num_inference_steps=50,
    generator=generator,
    strength=0.6,
).images
images[0]
```
</hfoption> This is the case for *IP-Adapter Plus* checkpoints which use the ViT-H image encoder.
<hfoption id="inpaint">
```py ```py
from diffusers import AutoPipelineForInpaint
import torch
from diffusers.utils import load_image
pipeline = AutoPipelineForInpaint.from_pretrained("runwayml/stable-diffusion-v1-5", torch_dtype=torch.float).to("cuda")
image = load_image("https://huggingface.co/datasets/YiYiXu/testing-images/resolve/main/inpaint_image.png")
mask = load_image("https://huggingface.co/datasets/YiYiXu/testing-images/resolve/main/mask.png")
ip_image = load_image("https://huggingface.co/datasets/YiYiXu/testing-images/resolve/main/girl.png")
image = image.resize((512, 768))
mask = mask.resize((512, 768))
pipeline.load_ip_adapter("h94/IP-Adapter", subfolder="models", weight_name="ip-adapter_sd15.bin")
generator = torch.Generator(device="cpu").manual_seed(33)
images = pipeline(
prompt='best quality, high quality',
image = image,
mask_image = mask,
ip_adapter_image=ip_image,
negative_prompt="monochrome, lowres, bad anatomy, worst quality, low quality",
num_inference_steps=50,
generator=generator,
strength=0.5,
).images
images[0]
```
</hfoption>
</hfoptions>
IP-Adapters can also be used with [SDXL](../api/pipelines/stable_diffusion/stable_diffusion_xl.md)
```python
from diffusers import AutoPipelineForText2Image
from diffusers.utils import load_image
import torch
pipeline = AutoPipelineForText2Image.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
torch_dtype=torch.float16
).to("cuda")
image = load_image("https://huggingface.co/datasets/sayakpaul/sample-datasets/resolve/main/watercolor_painting.jpeg")
pipeline.load_ip_adapter("h94/IP-Adapter", subfolder="sdxl_models", weight_name="ip-adapter_sdxl.bin")
generator = torch.Generator(device="cpu").manual_seed(33)
image = pipeline(
prompt="best quality, high quality",
ip_adapter_image=image,
negative_prompt="monochrome, lowres, bad anatomy, worst quality, low quality",
num_inference_steps=25,
generator=generator,
).images[0]
image.save("sdxl_t2i.png")
```
<div class="flex flex-row gap-4">
<div class="flex-1">
<img class="rounded-xl" src="https://huggingface.co/datasets/sayakpaul/sample-datasets/resolve/main/watercolor_painting.jpeg"/>
<figcaption class="mt-2 text-center text-sm text-gray-500">input image</figcaption>
</div>
<div class="flex-1">
<img class="rounded-xl" src="https://huggingface.co/datasets/sayakpaul/sample-datasets/resolve/main/sdxl_t2i.png"/>
<figcaption class="mt-2 text-center text-sm text-gray-500">adapted image</figcaption>
</div>
</div>
You can use the IP-Adapter face model to apply specific faces to your images. It is an effective way to maintain consistent characters in your image generations.
Weights are loaded with the same method used for the other IP-Adapters.
```python
# Load ip-adapter-full-face_sd15.bin
pipeline.load_ip_adapter("h94/IP-Adapter", subfolder="models", weight_name="ip-adapter-full-face_sd15.bin")
```
<Tip>
It is recommended to use `DDIMScheduler` and `EulerDiscreteScheduler` for face model.
</Tip>
```python
import torch
from diffusers import StableDiffusionPipeline, DDIMScheduler
from diffusers.utils import load_image
pipeline = StableDiffusionPipeline.from_pretrained(
"runwayml/stable-diffusion-v1-5",
torch_dtype=torch.float16,
).to("cuda")
pipeline.scheduler = DDIMScheduler.from_config(pipeline.scheduler.config)
pipeline.load_ip_adapter("h94/IP-Adapter", subfolder="models", weight_name="ip-adapter-full-face_sd15.bin")
pipeline.set_ip_adapter_scale(0.7)
image = load_image("https://huggingface.co/datasets/YiYiXu/testing-images/resolve/main/ai_face2.png")
generator = torch.Generator(device="cpu").manual_seed(33)
image = pipeline(
prompt="A photo of a girl wearing a black dress, holding red roses in hand, upper body, behind is the Eiffel Tower",
ip_adapter_image=image,
negative_prompt="monochrome, lowres, bad anatomy, worst quality, low quality",
num_inference_steps=50, num_images_per_prompt=1, width=512, height=704,
generator=generator,
).images[0]
```
<div class="flex flex-row gap-4">
<div class="flex-1">
<img class="rounded-xl" src="https://huggingface.co/datasets/YiYiXu/testing-images/resolve/main/ai_face2.png"/>
<figcaption class="mt-2 text-center text-sm text-gray-500">input image</figcaption>
</div>
<div class="flex-1">
<img class="rounded-xl" src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/ipadapter_full_face_output.png"/>
<figcaption class="mt-2 text-center text-sm text-gray-500">output image</figcaption>
</div>
</div>
You can load multiple IP-Adapter models and use multiple reference images at the same time. In this example we use IP-Adapter-Plus face model to create a consistent character and also use IP-Adapter-Plus model along with 10 images to create a coherent style in the image we generate.
```python
import torch
from diffusers import AutoPipelineForText2Image, DDIMScheduler
from transformers import CLIPVisionModelWithProjection
from diffusers.utils import load_image
image_encoder = CLIPVisionModelWithProjection.from_pretrained( image_encoder = CLIPVisionModelWithProjection.from_pretrained(
"h94/IP-Adapter", "h94/IP-Adapter",
subfolder="models/image_encoder", subfolder="models/image_encoder",
torch_dtype=torch.float16, torch_dtype=torch.float16
) )
pipeline = AutoPipelineForText2Image.from_pretrained( pipeline = AutoPipelineForText2Image.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0", "stabilityai/stable-diffusion-xl-base-1.0",
torch_dtype=torch.float16,
image_encoder=image_encoder, image_encoder=image_encoder,
) torch_dtype=torch.float16
pipeline.scheduler = DDIMScheduler.from_config(pipeline.scheduler.config) ).to("cuda")
pipeline.load_ip_adapter(
"h94/IP-Adapter",
subfolder="sdxl_models",
weight_name=["ip-adapter-plus_sdxl_vit-h.safetensors", "ip-adapter-plus-face_sdxl_vit-h.safetensors"]
)
pipeline.set_ip_adapter_scale([0.7, 0.3])
pipeline.enable_model_cpu_offload()
face_image = load_image("https://huggingface.co/datasets/YiYiXu/testing-images/resolve/main/women_input.png")
style_folder = "https://huggingface.co/datasets/YiYiXu/testing-images/resolve/main/style_ziggy"
style_images = [load_image(f"{style_folder}/img{i}.png") for i in range(10)]
generator = torch.Generator(device="cpu").manual_seed(0)
image = pipeline(
prompt="wonderwoman",
ip_adapter_image=[style_images, face_image],
negative_prompt="monochrome, lowres, bad anatomy, worst quality, low quality",
num_inference_steps=50, num_images_per_prompt=1,
generator=generator,
).images[0]
```
<div class="flex justify-center">
    <img src="https://huggingface.co/datasets/YiYiXu/testing-images/resolve/main/ip_style_grid.png" />
<figcaption class="mt-2 text-center text-sm text-gray-500">style input image</figcaption>
</div>
<div class="flex flex-row gap-4">
<div class="flex-1">
<img class="rounded-xl" src="https://huggingface.co/datasets/YiYiXu/testing-images/resolve/main/women_input.png"/>
<figcaption class="mt-2 text-center text-sm text-gray-500">face input image</figcaption>
</div>
<div class="flex-1">
<img class="rounded-xl" src="https://huggingface.co/datasets/YiYiXu/testing-images/resolve/main/ip_multi_out.png"/>
<figcaption class="mt-2 text-center text-sm text-gray-500">output image</figcaption>
</div>
</div>
### LCM-Lora
You can use IP-Adapter with LCM-Lora to achieve "instant fine-tune" with custom images. Note that you need to load IP-Adapter weights before loading the LCM-Lora weights.
```py
from diffusers import DiffusionPipeline, LCMScheduler
import torch
from diffusers.utils import load_image
model_id = "sd-dreambooth-library/herge-style"
lcm_lora_id = "latent-consistency/lcm-lora-sdv1-5"
pipe = DiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16)
pipe.load_ip_adapter("h94/IP-Adapter", subfolder="models", weight_name="ip-adapter_sd15.bin")
pipe.load_lora_weights(lcm_lora_id)
pipe.scheduler = LCMScheduler.from_config(pipe.scheduler.config)
pipe.enable_model_cpu_offload()
prompt = "best quality, high quality"
image = load_image("https://user-images.githubusercontent.com/24734142/266492875-2d50d223-8475-44f0-a7c6-08b51cb53572.png")
images = pipe(
prompt=prompt,
ip_adapter_image=image,
num_inference_steps=4,
guidance_scale=1,
).images[0]
```
### Other pipelines
IP-Adapter is compatible with any pipeline that (1) uses a text prompt and (2) uses Stable Diffusion or Stable Diffusion XL checkpoint. To use IP-Adapter with a different pipeline, all you need to do is to run `load_ip_adapter()` method after you create the pipeline, and then pass your image to the pipeline as `ip_adapter_image`
<Tip>
🤗 Diffusers currently only supports using IP-Adapter with some of the most popular pipelines, feel free to open a [feature request](https://github.com/huggingface/diffusers/issues/new/choose) if you have a cool use-case and require integrating IP-adapters with a pipeline that does not support it yet!
</Tip>
You can find below examples on how to use IP-Adapter with ControlNet and AnimateDiff.
<hfoptions id="model">
<hfoption id="ControlNet">
```
from diffusers import StableDiffusionControlNetPipeline, ControlNetModel
import torch
from diffusers.utils import load_image
controlnet_model_path = "lllyasviel/control_v11f1p_sd15_depth"
controlnet = ControlNetModel.from_pretrained(controlnet_model_path, torch_dtype=torch.float16)
pipeline = StableDiffusionControlNetPipeline.from_pretrained(
"runwayml/stable-diffusion-v1-5", controlnet=controlnet, torch_dtype=torch.float16)
pipeline.to("cuda")
image = load_image("https://huggingface.co/datasets/YiYiXu/testing-images/resolve/main/statue.png")
depth_map = load_image("https://huggingface.co/datasets/YiYiXu/testing-images/resolve/main/depth.png")
pipeline.load_ip_adapter("h94/IP-Adapter", subfolder="models", weight_name="ip-adapter_sd15.bin")
generator = torch.Generator(device="cpu").manual_seed(33)
images = pipeline(
prompt='best quality, high quality',
image=depth_map,
ip_adapter_image=image,
negative_prompt="monochrome, lowres, bad anatomy, worst quality, low quality",
num_inference_steps=50,
generator=generator,
).images
images[0]
```
<div class="flex flex-row gap-4">
<div class="flex-1">
<img class="rounded-xl" src="https://huggingface.co/datasets/YiYiXu/testing-images/resolve/main/statue.png"/>
<figcaption class="mt-2 text-center text-sm text-gray-500">input image</figcaption>
</div>
<div class="flex-1">
<img class="rounded-xl" src="https://huggingface.co/datasets/YiYiXu/testing-images/resolve/main/ipa-controlnet-out.png"/>
<figcaption class="mt-2 text-center text-sm text-gray-500">adapted image</figcaption>
</div>
</div>
</hfoption>
<hfoption id="AnimateDiff">
```py pipeline.load_ip_adapter("h94/IP-Adapter", subfolder="sdxl_models", weight_name="ip-adapter-plus_sdxl_vit-h.safetensors")
# animate diff + ip adapter
import torch
from diffusers import MotionAdapter, AnimateDiffPipeline, DDIMScheduler
from diffusers.utils import export_to_gif, load_image
# Load the motion adapter
adapter = MotionAdapter.from_pretrained("guoyww/animatediff-motion-adapter-v1-5-2", torch_dtype=torch.float16)
# load SD 1.5 based finetuned model
model_id = "Lykon/DreamShaper"
pipe = AnimateDiffPipeline.from_pretrained(model_id, motion_adapter=adapter, torch_dtype=torch.float16)
# scheduler
scheduler = DDIMScheduler(
clip_sample=False,
beta_start=0.00085,
beta_end=0.012,
beta_schedule="linear",
timestep_spacing="trailing",
steps_offset=1
)
pipe.scheduler = scheduler
# enable memory savings
pipe.enable_vae_slicing()
pipe.enable_model_cpu_offload()
# load ip_adapter
pipe.load_ip_adapter("h94/IP-Adapter", subfolder="models", weight_name="ip-adapter_sd15.bin")
# load motion adapters
pipe.load_lora_weights("guoyww/animatediff-motion-lora-zoom-out", adapter_name="zoom-out")
pipe.load_lora_weights("guoyww/animatediff-motion-lora-tilt-up", adapter_name="tilt-up")
pipe.load_lora_weights("guoyww/animatediff-motion-lora-pan-left", adapter_name="pan-left")
seed = 42
image = load_image("https://user-images.githubusercontent.com/24734142/266492875-2d50d223-8475-44f0-a7c6-08b51cb53572.png")
images = [image] * 3
prompts = ["best quality, high quality"] * 3
negative_prompt = "bad quality, worst quality"
adapter_weights = [[0.75, 0.0, 0.0], [0.0, 0.0, 0.75], [0.0, 0.75, 0.75]]
# generate
output_frames = []
for prompt, image, adapter_weight in zip(prompts, images, adapter_weights):
pipe.set_adapters(["zoom-out", "tilt-up", "pan-left"], adapter_weights=adapter_weight)
output = pipe(
prompt= prompt,
num_frames=16,
guidance_scale=7.5,
num_inference_steps=30,
ip_adapter_image = image,
generator=torch.Generator("cpu").manual_seed(seed),
)
frames = output.frames[0]
output_frames.extend(frames)
export_to_gif(output_frames, "test_out_animation.gif")
``` ```
</hfoption>
</hfoptions>
src/diffusers/loaders/ip_adapter.py
View file @ 9efe1e52
...@@ -180,6 +180,15 @@ class IPAdapterMixin: ...@@ -180,6 +180,15 @@ class IPAdapterMixin:
unet._load_ip_adapter_weights(state_dicts) unet._load_ip_adapter_weights(state_dicts)
def set_ip_adapter_scale(self, scale): def set_ip_adapter_scale(self, scale):
"""
Sets the conditioning scale between text and image.
Example:
```py
pipeline.set_ip_adapter_scale(0.5)
```
"""
unet = getattr(self, self.unet_name) if not hasattr(self, "unet") else self.unet unet = getattr(self, self.unet_name) if not hasattr(self, "unet") else self.unet
for attn_processor in unet.attn_processors.values(): for attn_processor in unet.attn_processors.values():
if isinstance(attn_processor, (IPAdapterAttnProcessor, IPAdapterAttnProcessor2_0)): if isinstance(attn_processor, (IPAdapterAttnProcessor, IPAdapterAttnProcessor2_0)):
......
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