Instructions to use baidu/ERNIE-Image-Turbo with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use baidu/ERNIE-Image-Turbo with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("baidu/ERNIE-Image-Turbo", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
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README.md
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<a href="https://www.modelscope.cn/models/PaddlePaddle/ERNIE-Image-Turbo/summary">🤖 ERNIE-Image-Turbo</a>
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<a href="https://huggingface.co/spaces/baidu/ERNIE-Image-Turbo">🖥️ Huggingface Demo1</a> |
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<a href="https://huggingface.co/spaces/akhaliq/ERNIE-Image-Turbo">🖥️ Huggingface Demo2(ZeroGPU)</a>
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<a href="https://github.com/baidu/ernie-image">Github</a> |
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<a href="https://aistudio.baidu.com/ernieimage">🖥️ AI Studio Demo</a> |
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<a href="https://yiyan.baidu.com/blog/posts/ernie-image">📖 Blog</a> |
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<a href="https://ernieimageprompt.com/">🖼️ Art Gallery</a>
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<a href="https://discord.gg/ByUTbjfG5k">🫨 Discord</a> |
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<a href="https://x.com/ErnieforDevs">🏷️ X</a>
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ERNIE-Image-Turbo is an open text-to-image generation model developed by the ERNIE-Image team at Baidu. It is the distilled release of ERNIE-Image, built on the same single-stream Diffusion Transformer (DiT) family and designed for fast generation with strong fidelity in only 8 inference steps. The model retains strong controllability in practical generation scenarios where accurate content realization matters as much as aesthetics. In particular, ERNIE-Image-Turbo remains strong on complex instruction following, text rendering, and structured image generation, making it well suited for posters, comics, multi-panel layouts, and other content creation tasks that require both visual quality and efficiency. It also supports a broad range of visual styles, including realistic photography, design-oriented imagery, and stylized aesthetic outputs.
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<p align="center">
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<a href="https://www.modelscope.cn/models/PaddlePaddle/ERNIE-Image-Turbo/summary">🤖 ERNIE-Image-Turbo</a>
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<br/>
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<a href="https://huggingface.co/spaces/baidu/ERNIE-Image-Turbo">🖥️ Huggingface Demo1</a> |
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<a href="https://huggingface.co/spaces/akhaliq/ERNIE-Image-Turbo">🖥️ Huggingface Demo2(ZeroGPU)</a> |
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<a href="https://aistudio.baidu.com/ernieimage">🖥️ AI Studio Demo</a>
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<a href="https://github.com/baidu/ernie-image">Github</a> |
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<a href="https://yiyan.baidu.com/blog/posts/ernie-image">📖 Blog</a> |
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<a href="https://ernieimageprompt.com/">🖼️ Art Gallery</a>
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<a href="https://discord.gg/ByUTbjfG5k">🫨 Discord</a> |
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<a href="https://x.com/ErnieforDevs">🏷️ X</a>
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</p>
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ERNIE-Image-Turbo is an open text-to-image generation model developed by the ERNIE-Image team at Baidu. It is the distilled release of ERNIE-Image, built on the same single-stream Diffusion Transformer (DiT) family and designed for fast generation with strong fidelity in only 8 inference steps. The model retains strong controllability in practical generation scenarios where accurate content realization matters as much as aesthetics. In particular, ERNIE-Image-Turbo remains strong on complex instruction following, text rendering, and structured image generation, making it well suited for posters, comics, multi-panel layouts, and other content creation tasks that require both visual quality and efficiency. It also supports a broad range of visual styles, including realistic photography, design-oriented imagery, and stylized aesthetic outputs.
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<p align="center">
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