Text-to-Image
Diffusers
Chinese
AltDiffusionPipeline
stable-diffusion
stable-diffusion-diffusers
bilingual
Chinese
en
English
Instructions to use BAAI/AltDiffusion with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use BAAI/AltDiffusion with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("BAAI/AltDiffusion", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
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README.md
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# 模型信息 Model Information
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我们使用 [AltCLIP](https://github.com/FlagAI-Open/FlagAI/tree/master/examples/AltCLIP/README.md)
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我们的版本在中英文对齐方面表现非常出色,是目前市面上开源的最强版本,保留了原版stable diffusion的大部分能力,并且在某些例子上比有着比原版模型更出色的能力。
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Our model performs well in aligning Chinese and English, and is the strongest open source version on the market today, retaining most of the stable diffusion capabilities of the original, and in some cases even better than the original model.
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We used [AltCLIP](https://github.com/FlagAI-Open/FlagAI/tree/master/examples/AltCLIP/README.md)
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AltDiffusion model is backed by a bilingual CLIP model named AltCLIP, which is also accessible in FlagAI. You can read [this tutorial](https://github.com/FlagAI-Open/FlagAI/tree/master/examples/AltCLIP/README.md) for more information.
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# 模型信息 Model Information
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我们使用 [AltCLIP](https://github.com/FlagAI-Open/FlagAI/tree/master/examples/AltCLIP/README.md),基于 [Stable Diffusion](https://huggingface.co/CompVis/stable-diffusion) 训练了双语Diffusion模型,训练数据来自 [WuDao数据集](https://data.baai.ac.cn/details/WuDaoCorporaText) 和 [LAION](https://huggingface.co/datasets/ChristophSchuhmann/improved_aesthetics_6plus) 。
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我们的版本在中英文对齐方面表现非常出色,是目前市面上开源的最强版本,保留了原版stable diffusion的大部分能力,并且在某些例子上比有着比原版模型更出色的能力。
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Our model performs well in aligning Chinese and English, and is the strongest open source version on the market today, retaining most of the stable diffusion capabilities of the original, and in some cases even better than the original model.
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We used [AltCLIP](https://github.com/FlagAI-Open/FlagAI/tree/master/examples/AltCLIP/README.md), and trained a bilingual Diffusion model based on [Stable Diffusion](https://huggingface.co/CompVis/stable-diffusion), with training data from [WuDao dataset](https://data.baai.ac.cn/details/WuDaoCorporaText) and [LAION](https://huggingface.co/datasets/laion/laion2B-en).
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AltDiffusion model is backed by a bilingual CLIP model named AltCLIP, which is also accessible in FlagAI. You can read [this tutorial](https://github.com/FlagAI-Open/FlagAI/tree/master/examples/AltCLIP/README.md) for more information.
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