Unconditional Image Generation
Diffusers
Safetensors
English
pixeldit2
image-generation
class-conditional
imagenet
flow-matching
pixel-space
Instructions to use BiliSakura/PixelDiT2-diffusers with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use BiliSakura/PixelDiT2-diffusers with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("BiliSakura/PixelDiT2-diffusers", dtype=torch.bfloat16, device_map="cuda") prompt = "golden retriever" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
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license_link: https://huggingface.co/nvidia/PixelDiT2-ImageNet
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library_name: diffusers
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tags:
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inference: true
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---
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# BiliSakura/PixelDiT2-diffusers
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## License
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Pipeline code in this repository is Apache-2.0. Converted weights remain under the NVIDIA NSCLv1 non-commercial research license of [nvidia/PixelDiT2-ImageNet](https://huggingface.co/nvidia/PixelDiT2-ImageNet).
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license_link: https://huggingface.co/nvidia/PixelDiT2-ImageNet
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library_name: diffusers
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tags:
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- diffusers
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- pixeldit2
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- image-generation
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- class-conditional
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- imagenet
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- flow-matching
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- pixel-space
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inference: true
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widget:
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- text: golden retriever
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output:
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url: PixelDiT2-H-16-256/demo.png
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- text: golden retriever
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output:
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url: PixelDiT2-H-16-512/demo.png
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language:
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- en
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pipeline_tag: unconditional-image-generation
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---
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# BiliSakura/PixelDiT2-diffusers
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## License
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Pipeline code in this repository is Apache-2.0. Converted weights remain under the NVIDIA NSCLv1 non-commercial research license of [nvidia/PixelDiT2-ImageNet](https://huggingface.co/nvidia/PixelDiT2-ImageNet).
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