| <!--Copyright 2023 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. |
| --> |
|
|
| # AltDiffusion |
|
|
| AltDiffusion was proposed in [AltCLIP: Altering the Language Encoder in CLIP for Extended Language Capabilities](https://huggingface.co/papers/2211.06679) by Zhongzhi Chen, Guang Liu, Bo-Wen Zhang, Fulong Ye, Qinghong Yang, Ledell Wu. |
|
|
| The abstract from the paper is: |
|
|
| *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.* |
|
|
| ## Tips |
|
|
| `AltDiffusion` is conceptually the same as [Stable Diffusion](./stable_diffusion/overview). |
|
|
| <Tip> |
|
|
| Make sure to check out the Schedulers [guide](/using-diffusers/schedulers) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](/using-diffusers/loading#reuse-components-across-pipelines) section to learn how to efficiently load the same components into multiple pipelines. |
|
|
| </Tip> |
|
|
| ## AltDiffusionPipeline |
|
|
| [[autodoc]] AltDiffusionPipeline |
| - all |
| - __call__ |
|
|
| ## AltDiffusionImg2ImgPipeline |
|
|
| [[autodoc]] AltDiffusionImg2ImgPipeline |
| - all |
| - __call__ |
|
|
| ## AltDiffusionPipelineOutput |
|
|
| [[autodoc]] pipelines.alt_diffusion.AltDiffusionPipelineOutput |
| - all |
| - __call__ |
