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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## Released Versions
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## Benchmark
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## Released Versions
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[ERNIE-Image](https://huggingface.co/Baidu/ERNIE-Image): Our **SFT model**, delivers stronger general-purpose capability and instruction fidelity in typically **50 inference steps**.
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[ERNIE-Image-Turbo](https://huggingface.co/Baidu/ERNIE-Image-Turbo): Our **Turbo model**, optimized by **DMD and RL**, achieves faster speed and higher aesthetics in only **8 inference steps**.
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## Benchmark
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