Unconditional Image Generation
Diffusers
Safetensors
English
image-generation
class-conditional
dit-moe
Instructions to use BiliSakura/DiT-MoE-diffusers with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use BiliSakura/DiT-MoE-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/DiT-MoE-diffusers", 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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- class-conditional
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pipeline_tag: unconditional-image-generation
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---
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# DiT-MoE-diffusers
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- class-conditional
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- dit-moe
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pipeline_tag: unconditional-image-generation
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widget:
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- output:
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url: DiT-MoE-XL-8E2A/demo.png
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# DiT-MoE-diffusers
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