Instructions to use lavinal712/transfusion-vae with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use lavinal712/transfusion-vae with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("lavinal712/transfusion-vae", 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
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README.md
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@@ -52,4 +52,6 @@ Paper: [Transfusion: Predict the Next Token and Diffuse Images with One Multi-Mo
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Dataset: [ImageNet](https://image-net.org/), [COCO](https://cocodataset.org/), [FFHQ](https://github.com/NVlabs/ffhq-dataset)
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Base Code: [lavinal712/AutoencoderKL](https://github.com/lavinal712/AutoencoderKL)
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Dataset: [ImageNet](https://image-net.org/), [COCO](https://cocodataset.org/), [FFHQ](https://github.com/NVlabs/ffhq-dataset)
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Base Code: [lavinal712/AutoencoderKL](https://github.com/lavinal712/AutoencoderKL)
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Training Code: [lavinal712/AutoencoderKL/tree/transfusion_vae](https://github.com/lavinal712/AutoencoderKL/tree/transfusion_vae)
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