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[docs] T2I (#7623)

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* refactor t2i

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docs/source/en/_toctree.yml
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title: Kandinsky
- local: using-diffusers/controlnet
title: ControlNet
- local: using-diffusers/t2i_adapter
title: T2I-Adapter
- local: using-diffusers/shap-e
title: Shap-E
- local: using-diffusers/diffedit
@@ -358,7 +360,7 @@
- local: api/pipelines/stable_diffusion/ldm3d_diffusion
title: LDM3D Text-to-(RGB, Depth), Text-to-(RGB-pano, Depth-pano), LDM3D Upscaler
- local: api/pipelines/stable_diffusion/adapter
title: Stable Diffusion T2I-Adapter
title: T2I-Adapter
- local: api/pipelines/stable_diffusion/gligen
title: GLIGEN (Grounded Language-to-Image Generation)
title: Stable Diffusion

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docs/source/en/api/pipelines/stable_diffusion/adapter.md
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@@ -10,9 +10,7 @@ an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express o
specific language governing permissions and limitations under the License.
-->
# Text-to-Image Generation with Adapter Conditioning
## Overview
# T2I-Adapter
[T2I-Adapter: Learning Adapters to Dig out More Controllable Ability for Text-to-Image Diffusion Models](https://arxiv.org/abs/2302.08453) by Chong Mou, Xintao Wang, Liangbin Xie, Jian Zhang, Zhongang Qi, Ying Shan, Xiaohu Qie.
@@ -24,236 +22,26 @@ The abstract of the paper is the following:
This model was contributed by the community contributor [HimariO](https://github.com/HimariO) ❤️ .
## Available Pipelines:
| Pipeline | Tasks | Demo
|---|---|:---:|
| [StableDiffusionAdapterPipeline](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/t2i_adapter/pipeline_stable_diffusion_adapter.py) | *Text-to-Image Generation with T2I-Adapter Conditioning* | -
| [StableDiffusionXLAdapterPipeline](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/t2i_adapter/pipeline_stable_diffusion_xl_adapter.py) | *Text-to-Image Generation with T2I-Adapter Conditioning on StableDiffusion-XL* | -
## Usage example with the base model of StableDiffusion-1.4/1.5
In the following we give a simple example of how to use a *T2I-Adapter* checkpoint with Diffusers for inference based on StableDiffusion-1.4/1.5.
All adapters use the same pipeline.
1. Images are first converted into the appropriate *control image* format.
2. The *control image* and *prompt* are passed to the [`StableDiffusionAdapterPipeline`].
Let's have a look at a simple example using the [Color Adapter](https://huggingface.co/TencentARC/t2iadapter_color_sd14v1).
```python
from diffusers.utils import load_image, make_image_grid
image = load_image("https://huggingface.co/datasets/diffusers/docs-images/resolve/main/t2i-adapter/color_ref.png")
```
![img](https://huggingface.co/datasets/diffusers/docs-images/resolve/main/t2i-adapter/color_ref.png)
Then we can create our color palette by simply resizing it to 8 by 8 pixels and then scaling it back to original size.
```python
from PIL import Image
color_palette = image.resize((8, 8))
color_palette = color_palette.resize((512, 512), resample=Image.Resampling.NEAREST)
```
Let's take a look at the processed image.
![img](https://huggingface.co/datasets/diffusers/docs-images/resolve/main/t2i-adapter/color_palette.png)
Next, create the adapter pipeline
```py
import torch
from diffusers import StableDiffusionAdapterPipeline, T2IAdapter
adapter = T2IAdapter.from_pretrained("TencentARC/t2iadapter_color_sd14v1", torch_dtype=torch.float16)
pipe = StableDiffusionAdapterPipeline.from_pretrained(
"CompVis/stable-diffusion-v1-4",
adapter=adapter,
torch_dtype=torch.float16,
)
pipe.to("cuda")
```
Finally, pass the prompt and control image to the pipeline
```py
# fix the random seed, so you will get the same result as the example
generator = torch.Generator("cuda").manual_seed(7)
out_image = pipe(
"At night, glowing cubes in front of the beach",
image=color_palette,
generator=generator,
).images[0]
make_image_grid([image, color_palette, out_image], rows=1, cols=3)
```
![img](https://huggingface.co/datasets/diffusers/docs-images/resolve/main/t2i-adapter/color_output.png)
## Usage example with the base model of StableDiffusion-XL
In the following we give a simple example of how to use a *T2I-Adapter* checkpoint with Diffusers for inference based on StableDiffusion-XL.
All adapters use the same pipeline.
1. Images are first downloaded into the appropriate *control image* format.
2. The *control image* and *prompt* are passed to the [`StableDiffusionXLAdapterPipeline`].
Let's have a look at a simple example using the [Sketch Adapter](https://huggingface.co/Adapter/t2iadapter/tree/main/sketch_sdxl_1.0).
```python
from diffusers.utils import load_image, make_image_grid
sketch_image = load_image("https://huggingface.co/Adapter/t2iadapter/resolve/main/sketch.png").convert("L")
```
![img](https://huggingface.co/Adapter/t2iadapter/resolve/main/sketch.png)
Then, create the adapter pipeline
```py
import torch
from diffusers import (
T2IAdapter,
StableDiffusionXLAdapterPipeline,
DDPMScheduler
)
model_id = "stabilityai/stable-diffusion-xl-base-1.0"
adapter = T2IAdapter.from_pretrained("Adapter/t2iadapter", subfolder="sketch_sdxl_1.0", torch_dtype=torch.float16, adapter_type="full_adapter_xl")
scheduler = DDPMScheduler.from_pretrained(model_id, subfolder="scheduler")
pipe = StableDiffusionXLAdapterPipeline.from_pretrained(
model_id, adapter=adapter, safety_checker=None, torch_dtype=torch.float16, variant="fp16", scheduler=scheduler
)
pipe.to("cuda")
```
Finally, pass the prompt and control image to the pipeline
```py
# fix the random seed, so you will get the same result as the example
generator = torch.Generator().manual_seed(42)
sketch_image_out = pipe(
prompt="a photo of a dog in real world, high quality",
negative_prompt="extra digit, fewer digits, cropped, worst quality, low quality",
image=sketch_image,
generator=generator,
guidance_scale=7.5
).images[0]
make_image_grid([sketch_image, sketch_image_out], rows=1, cols=2)
```
![img](https://huggingface.co/Adapter/t2iadapter/resolve/main/sketch_output.png)
## Available checkpoints
Non-diffusers checkpoints can be found under [TencentARC/T2I-Adapter](https://huggingface.co/TencentARC/T2I-Adapter/tree/main/models).
### T2I-Adapter with Stable Diffusion 1.4
| Model Name | Control Image Overview| Control Image Example | Generated Image Example |
|---|---|---|---|
|[TencentARC/t2iadapter_color_sd14v1](https://huggingface.co/TencentARC/t2iadapter_color_sd14v1)<br/> *Trained with spatial color palette* | An image with 8x8 color palette.|<a href="https://huggingface.co/datasets/diffusers/docs-images/resolve/main/t2i-adapter/color_sample_input.png"><img width="64" style="margin:0;padding:0;" src="https://huggingface.co/datasets/diffusers/docs-images/resolve/main/t2i-adapter/color_sample_input.png"/></a>|<a href="https://huggingface.co/datasets/diffusers/docs-images/resolve/main/t2i-adapter/color_sample_output.png"><img width="64" src="https://huggingface.co/datasets/diffusers/docs-images/resolve/main/t2i-adapter/color_sample_output.png"/></a>|
|[TencentARC/t2iadapter_canny_sd14v1](https://huggingface.co/TencentARC/t2iadapter_canny_sd14v1)<br/> *Trained with canny edge detection* | A monochrome image with white edges on a black background.|<a href="https://huggingface.co/datasets/diffusers/docs-images/resolve/main/t2i-adapter/canny_sample_input.png"><img width="64" style="margin:0;padding:0;" src="https://huggingface.co/datasets/diffusers/docs-images/resolve/main/t2i-adapter/canny_sample_input.png"/></a>|<a href="https://huggingface.co/datasets/diffusers/docs-images/resolve/main/t2i-adapter/canny_sample_output.png"><img width="64" src="https://huggingface.co/datasets/diffusers/docs-images/resolve/main/t2i-adapter/canny_sample_output.png"/></a>|
|[TencentARC/t2iadapter_sketch_sd14v1](https://huggingface.co/TencentARC/t2iadapter_sketch_sd14v1)<br/> *Trained with [PidiNet](https://github.com/zhuoinoulu/pidinet) edge detection* | A hand-drawn monochrome image with white outlines on a black background.|<a href="https://huggingface.co/datasets/diffusers/docs-images/resolve/main/t2i-adapter/sketch_sample_input.png"><img width="64" style="margin:0;padding:0;" src="https://huggingface.co/datasets/diffusers/docs-images/resolve/main/t2i-adapter/sketch_sample_input.png"/></a>|<a href="https://huggingface.co/datasets/diffusers/docs-images/resolve/main/t2i-adapter/sketch_sample_output.png"><img width="64" src="https://huggingface.co/datasets/diffusers/docs-images/resolve/main/t2i-adapter/sketch_sample_output.png"/></a>|
|[TencentARC/t2iadapter_depth_sd14v1](https://huggingface.co/TencentARC/t2iadapter_depth_sd14v1)<br/> *Trained with Midas depth estimation* | A grayscale image with black representing deep areas and white representing shallow areas.|<a href="https://huggingface.co/datasets/diffusers/docs-images/resolve/main/t2i-adapter/depth_sample_input.png"><img width="64" src="https://huggingface.co/datasets/diffusers/docs-images/resolve/main/t2i-adapter/depth_sample_input.png"/></a>|<a href="https://huggingface.co/datasets/diffusers/docs-images/resolve/main/t2i-adapter/depth_sample_output.png"><img width="64" src="https://huggingface.co/datasets/diffusers/docs-images/resolve/main/t2i-adapter/depth_sample_output.png"/></a>|
|[TencentARC/t2iadapter_openpose_sd14v1](https://huggingface.co/TencentARC/t2iadapter_openpose_sd14v1)<br/> *Trained with OpenPose bone image* | A [OpenPose bone](https://github.com/CMU-Perceptual-Computing-Lab/openpose) image.|<a href="https://huggingface.co/datasets/diffusers/docs-images/resolve/main/t2i-adapter/openpose_sample_input.png"><img width="64" src="https://huggingface.co/datasets/diffusers/docs-images/resolve/main/t2i-adapter/openpose_sample_input.png"/></a>|<a href="https://huggingface.co/datasets/diffusers/docs-images/resolve/main/t2i-adapter/openpose_sample_output.png"><img width="64" src="https://huggingface.co/datasets/diffusers/docs-images/resolve/main/t2i-adapter/openpose_sample_output.png"/></a>|
|[TencentARC/t2iadapter_keypose_sd14v1](https://huggingface.co/TencentARC/t2iadapter_keypose_sd14v1)<br/> *Trained with mmpose skeleton image* | A [mmpose skeleton](https://github.com/open-mmlab/mmpose) image.|<a href="https://huggingface.co/datasets/diffusers/docs-images/resolve/main/t2i-adapter/keypose_sample_input.png"><img width="64" src="https://huggingface.co/datasets/diffusers/docs-images/resolve/main/t2i-adapter/keypose_sample_input.png"/></a>|<a href="https://huggingface.co/datasets/diffusers/docs-images/resolve/main/t2i-adapter/keypose_sample_output.png"><img width="64" src="https://huggingface.co/datasets/diffusers/docs-images/resolve/main/t2i-adapter/keypose_sample_output.png"/></a>|
|[TencentARC/t2iadapter_seg_sd14v1](https://huggingface.co/TencentARC/t2iadapter_seg_sd14v1)<br/>*Trained with semantic segmentation* | An [custom](https://github.com/TencentARC/T2I-Adapter/discussions/25) segmentation protocol image.|<a href="https://huggingface.co/datasets/diffusers/docs-images/resolve/main/t2i-adapter/seg_sample_input.png"><img width="64" src="https://huggingface.co/datasets/diffusers/docs-images/resolve/main/t2i-adapter/seg_sample_input.png"/></a>|<a href="https://huggingface.co/datasets/diffusers/docs-images/resolve/main/t2i-adapter/seg_sample_output.png"><img width="64" src="https://huggingface.co/datasets/diffusers/docs-images/resolve/main/t2i-adapter/seg_sample_output.png"/></a> |
|[TencentARC/t2iadapter_canny_sd15v2](https://huggingface.co/TencentARC/t2iadapter_canny_sd15v2)||
|[TencentARC/t2iadapter_depth_sd15v2](https://huggingface.co/TencentARC/t2iadapter_depth_sd15v2)||
|[TencentARC/t2iadapter_sketch_sd15v2](https://huggingface.co/TencentARC/t2iadapter_sketch_sd15v2)||
|[TencentARC/t2iadapter_zoedepth_sd15v1](https://huggingface.co/TencentARC/t2iadapter_zoedepth_sd15v1)||
|[Adapter/t2iadapter, subfolder='sketch_sdxl_1.0'](https://huggingface.co/Adapter/t2iadapter/tree/main/sketch_sdxl_1.0)||
|[Adapter/t2iadapter, subfolder='canny_sdxl_1.0'](https://huggingface.co/Adapter/t2iadapter/tree/main/canny_sdxl_1.0)||
|[Adapter/t2iadapter, subfolder='openpose_sdxl_1.0'](https://huggingface.co/Adapter/t2iadapter/tree/main/openpose_sdxl_1.0)||
## Combining multiple adapters
[`MultiAdapter`] can be used for applying multiple conditionings at once.
Here we use the keypose adapter for the character posture and the depth adapter for creating the scene.
```py
from diffusers.utils import load_image, make_image_grid
cond_keypose = load_image(
"https://huggingface.co/datasets/diffusers/docs-images/resolve/main/t2i-adapter/keypose_sample_input.png"
)
cond_depth = load_image(
"https://huggingface.co/datasets/diffusers/docs-images/resolve/main/t2i-adapter/depth_sample_input.png"
)
cond = [cond_keypose, cond_depth]
prompt = ["A man walking in an office room with a nice view"]
```
The two control images look as such:
![img](https://huggingface.co/datasets/diffusers/docs-images/resolve/main/t2i-adapter/keypose_sample_input.png)
![img](https://huggingface.co/datasets/diffusers/docs-images/resolve/main/t2i-adapter/depth_sample_input.png)
`MultiAdapter` combines keypose and depth adapters.
`adapter_conditioning_scale` balances the relative influence of the different adapters.
```py
import torch
from diffusers import StableDiffusionAdapterPipeline, MultiAdapter, T2IAdapter
adapters = MultiAdapter(
[
T2IAdapter.from_pretrained("TencentARC/t2iadapter_keypose_sd14v1"),
T2IAdapter.from_pretrained("TencentARC/t2iadapter_depth_sd14v1"),
]
)
adapters = adapters.to(torch.float16)
pipe = StableDiffusionAdapterPipeline.from_pretrained(
"CompVis/stable-diffusion-v1-4",
torch_dtype=torch.float16,
adapter=adapters,
).to("cuda")
image = pipe(prompt, cond, adapter_conditioning_scale=[0.8, 0.8]).images[0]
make_image_grid([cond_keypose, cond_depth, image], rows=1, cols=3)
```
![img](https://huggingface.co/datasets/diffusers/docs-images/resolve/main/t2i-adapter/keypose_depth_sample_output.png)
## T2I-Adapter vs ControlNet
T2I-Adapter is similar to [ControlNet](https://huggingface.co/docs/diffusers/main/en/api/pipelines/controlnet).
T2I-Adapter uses a smaller auxiliary network which is only run once for the entire diffusion process.
However, T2I-Adapter performs slightly worse than ControlNet.
## StableDiffusionAdapterPipeline
[[autodoc]] StableDiffusionAdapterPipeline
- all
- __call__
- enable_attention_slicing
- disable_attention_slicing
- enable_vae_slicing
- disable_vae_slicing
- enable_xformers_memory_efficient_attention
- disable_xformers_memory_efficient_attention
- all
- __call__
- enable_attention_slicing
- disable_attention_slicing
- enable_vae_slicing
- disable_vae_slicing
- enable_xformers_memory_efficient_attention
- disable_xformers_memory_efficient_attention
## StableDiffusionXLAdapterPipeline
[[autodoc]] StableDiffusionXLAdapterPipeline
- all
- __call__
- enable_attention_slicing
- disable_attention_slicing
- enable_vae_slicing
- disable_vae_slicing
- enable_xformers_memory_efficient_attention
- disable_xformers_memory_efficient_attention
- all
- __call__
- enable_attention_slicing
- disable_attention_slicing
- enable_vae_slicing
- disable_vae_slicing
- enable_xformers_memory_efficient_attention
- disable_xformers_memory_efficient_attention

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<!--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.
-->
# T2I-Adapter
[T2I-Adapter](https://hf.co/papers/2302.08453) is a lightweight adapter for controlling and providing more accurate
structure guidance for text-to-image models. It works by learning an alignment between the internal knowledge of the
text-to-image model and an external control signal, such as edge detection or depth estimation.
The T2I-Adapter design is simple, the condition is passed to four feature extraction blocks and three downsample
blocks. This makes it fast and easy to train different adapters for different conditions which can be plugged into the
text-to-image model. T2I-Adapter is similar to [ControlNet](controlnet) except it is smaller (~77M parameters) and
faster because it only runs once during the diffusion process. The downside is that performance may be slightly worse
than ControlNet.
This guide will show you how to use T2I-Adapter with different Stable Diffusion models and how you can compose multiple
T2I-Adapters to impose more than one condition.
> [!TIP]
> There are several T2I-Adapters available for different conditions, such as color palette, depth, sketch, pose, and
> segmentation. Check out the [TencentARC](https://hf.co/TencentARC) repository to try them out!
Before you begin, make sure you have the following libraries installed.
```py
# uncomment to install the necessary libraries in Colab
#!pip install -q diffusers accelerate controlnet-aux==0.0.7
```
## Text-to-image
Text-to-image models rely on a prompt to generate an image, but sometimes, text alone may not be enough to provide more
accurate structural guidance. T2I-Adapter allows you to provide an additional control image to guide the generation
process. For example, you can provide a canny image (a white outline of an image on a black background) to guide the
model to generate an image with a similar structure.
<hfoptions id="stablediffusion">
<hfoption id="Stable Diffusion 1.5">
Create a canny image with the [opencv-library](https://github.com/opencv/opencv-python).
```py
import cv2
import numpy as np
from PIL import Image
from diffusers.utils import load_image
image = load_image("https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/hf-logo.png")
image = np.array(image)
low_threshold = 100
high_threshold = 200
image = cv2.Canny(image, low_threshold, high_threshold)
image = Image.fromarray(image)
```
Now load a T2I-Adapter conditioned on [canny images](https://hf.co/TencentARC/t2iadapter_canny_sd15v2) and pass it to
the [`StableDiffusionAdapterPipeline`].
```py
import torch
from diffusers import StableDiffusionAdapterPipeline, T2IAdapter
adapter = T2IAdapter.from_pretrained("TencentARC/t2iadapter_canny_sd15v2", torch_dtype=torch.float16)
pipeline = StableDiffusionAdapterPipeline.from_pretrained(
"runwayml/stable-diffusion-v1-5",
adapter=adapter,
torch_dtype=torch.float16,
)
pipeline.to("cuda")
```
Finally, pass your prompt and control image to the pipeline.
```py
generator = torch.Generator("cuda").manual_seed(0)
image = pipeline(
prompt="cinematic photo of a plush and soft midcentury style rug on a wooden floor, 35mm photograph, film, professional, 4k, highly detailed",
image=image,
generator=generator,
).images[0]
image
```
<div class="flex justify-center">
<img class="rounded-xl" src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/t2i-sd1.5.png"/>
</div>
</hfoption>
<hfoption id="Stable Diffusion XL">
Create a canny image with the [controlnet-aux](https://github.com/huggingface/controlnet_aux) library.
```py
from controlnet_aux.canny import CannyDetector
from diffusers.utils import load_image
canny_detector = CannyDetector()
image = load_image("https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/hf-logo.png")
image = canny_detector(image, detect_resolution=384, image_resolution=1024)
```
Now load a T2I-Adapter conditioned on [canny images](https://hf.co/TencentARC/t2i-adapter-canny-sdxl-1.0) and pass it
to the [`StableDiffusionXLAdapterPipeline`].
```py
import torch
from diffusers import StableDiffusionXLAdapterPipeline, T2IAdapter, EulerAncestralDiscreteScheduler, AutoencoderKL
scheduler = EulerAncestralDiscreteScheduler.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="scheduler")
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
adapter = T2IAdapter.from_pretrained("TencentARC/t2i-adapter-canny-sdxl-1.0", torch_dtype=torch.float16)
pipeline = StableDiffusionXLAdapterPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
adapter=adapter,
vae=vae,
scheduler=scheduler,
torch_dtype=torch.float16,
variant="fp16",
)
pipeline.to("cuda")
```
Finally, pass your prompt and control image to the pipeline.
```py
generator = torch.Generator("cuda").manual_seed(0)
image = pipeline(
prompt="cinematic photo of a plush and soft midcentury style rug on a wooden floor, 35mm photograph, film, professional, 4k, highly detailed",
image=image,
generator=generator,
).images[0]
image
```
<div class="flex justify-center">
<img class="rounded-xl" src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/t2i-sdxl.png"/>
</div>
</hfoption>
</hfoptions>
## MultiAdapter
T2I-Adapters are also composable, allowing you to use more than one adapter to impose multiple control conditions on an
image. For example, you can use a pose map to provide structural control and a depth map for depth control. This is
enabled by the [`MultiAdapter`] class.
Let's condition a text-to-image model with a pose and depth adapter. Create and place your depth and pose image and in a list.
```py
from diffusers.utils import load_image
pose_image = load_image(
"https://huggingface.co/datasets/diffusers/docs-images/resolve/main/t2i-adapter/keypose_sample_input.png"
)
depth_image = load_image(
"https://huggingface.co/datasets/diffusers/docs-images/resolve/main/t2i-adapter/depth_sample_input.png"
)
cond = [pose_image, depth_image]
prompt = ["Santa Claus walking into an office room with a beautiful city view"]
```
<div class="flex gap-4">
<div>
<img class="rounded-xl" src="https://huggingface.co/datasets/diffusers/docs-images/resolve/main/t2i-adapter/depth_sample_input.png"/>
<figcaption class="mt-2 text-center text-sm text-gray-500">depth image</figcaption>
</div>
<div>
<img class="rounded-xl" src="https://huggingface.co/datasets/diffusers/docs-images/resolve/main/t2i-adapter/keypose_sample_input.png"/>
<figcaption class="mt-2 text-center text-sm text-gray-500">pose image</figcaption>
</div>
</div>
Load the corresponding pose and depth adapters as a list in the [`MultiAdapter`] class.
```py
import torch
from diffusers import StableDiffusionAdapterPipeline, MultiAdapter, T2IAdapter
adapters = MultiAdapter(
[
T2IAdapter.from_pretrained("TencentARC/t2iadapter_keypose_sd14v1"),
T2IAdapter.from_pretrained("TencentARC/t2iadapter_depth_sd14v1"),
]
)
adapters = adapters.to(torch.float16)
```
Finally, load a [`StableDiffusionAdapterPipeline`] with the adapters, and pass your prompt and conditioned images to
it. Use the [`adapter_conditioning_scale`] to adjust the weight of each adapter on the image.
```py
pipeline = StableDiffusionAdapterPipeline.from_pretrained(
"CompVis/stable-diffusion-v1-4",
torch_dtype=torch.float16,
adapter=adapters,
).to("cuda")
image = pipeline(prompt, cond, adapter_conditioning_scale=[0.7, 0.7]).images[0]
image
```
<div class="flex justify-center">
<img class="rounded-xl" src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/t2i-multi.png"/>
</div>