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# AltDiffusion

AltDiffusion was proposed in [AltCLIP: Altering the Language Encoder in CLIP for Extended Language Capabilities](https://arxiv.org/abs/2211.06679) by Zhongzhi Chen, Guang Liu, Bo-Wen Zhang, Fulong Ye, Qinghong Yang, Ledell Wu.

The abstract of the paper is the following:

*In this work, we present a conceptually simple and effective method to train a strong bilingual multimodal representation model. Starting from the pretrained multimodal representation model CLIP released by OpenAI, we switched its text encoder with a pretrained multilingual text encoder XLM-R, and aligned both languages and image representations by a two-stage training schema consisting of teacher learning and contrastive learning. We validate our method through evaluations of a wide range of tasks. We set new state-of-the-art performances on a bunch of tasks including ImageNet-CN, Flicker30k- CN, and COCO-CN. Further, we obtain very close performances with CLIP on almost all tasks, suggesting that one can simply alter the text encoder in CLIP for extended capabilities such as multilingual understanding.*


*Overview*:

| Pipeline | Tasks | Colab | Demo
|---|---|:---:|:---:|
| [pipeline_alt_diffusion.py](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/alt_diffusion/pipeline_alt_diffusion.py) | *Text-to-Image Generation* | - | -
| [pipeline_alt_diffusion_img2img.py](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/alt_diffusion/pipeline_alt_diffusion_img2img.py) | *Image-to-Image Text-Guided Generation* | - |-

## Tips

- AltDiffusion is conceptually exactly the same as [Stable Diffusion](./stable_diffusion/overview).

- *Run AltDiffusion*

AltDiffusion can be tested very easily with the [`AltDiffusionPipeline`], [`AltDiffusionImg2ImgPipeline`] and the `"BAAI/AltDiffusion-m9"` checkpoint exactly in the same way it is shown in the [Conditional Image Generation Guide](../../using-diffusers/conditional_image_generation) and the [Image-to-Image Generation Guide](../../using-diffusers/img2img).

- *How to load and use different schedulers.*

The alt diffusion pipeline uses [`DDIMScheduler`] scheduler by default. But `diffusers` provides many other schedulers that can be used with the alt diffusion pipeline such as [`PNDMScheduler`], [`LMSDiscreteScheduler`], [`EulerDiscreteScheduler`], [`EulerAncestralDiscreteScheduler`] etc.
To use a different scheduler, you can either change it via the [`ConfigMixin.from_config`] method or pass the `scheduler` argument to the `from_pretrained` method of the pipeline. For example, to use the [`EulerDiscreteScheduler`], you can do the following:

```python
>>> from diffusers import AltDiffusionPipeline, EulerDiscreteScheduler

>>> pipeline = AltDiffusionPipeline.from_pretrained("BAAI/AltDiffusion-m9")
>>> pipeline.scheduler = EulerDiscreteScheduler.from_config(pipeline.scheduler.config)

>>> # or
>>> euler_scheduler = EulerDiscreteScheduler.from_pretrained("BAAI/AltDiffusion-m9", subfolder="scheduler")
>>> pipeline = AltDiffusionPipeline.from_pretrained("BAAI/AltDiffusion-m9", scheduler=euler_scheduler)
```


- *How to convert all use cases with multiple or single pipeline*

If you want to use all possible use cases in a single `DiffusionPipeline` we recommend using the `components` functionality to instantiate all components in the most memory-efficient way:

```python
>>> from diffusers import (
...     AltDiffusionPipeline,
...     AltDiffusionImg2ImgPipeline,
... )

>>> text2img = AltDiffusionPipeline.from_pretrained("BAAI/AltDiffusion-m9")
>>> img2img = AltDiffusionImg2ImgPipeline(**text2img.components)

>>> # now you can use text2img(...) and img2img(...) just like the call methods of each respective pipeline
```

## AltDiffusionPipelineOutput
[[autodoc]] pipelines.alt_diffusion.AltDiffusionPipelineOutput
	- all
	- __call__

## AltDiffusionPipeline
[[autodoc]] AltDiffusionPipeline
	- all
	- __call__

## AltDiffusionImg2ImgPipeline
[[autodoc]] AltDiffusionImg2ImgPipeline
	- all
	- __call__
