Instructions to use spacepxl/sd-vae-REPA-E-e2e-diffusers with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use spacepxl/sd-vae-REPA-E-e2e-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("spacepxl/sd-vae-REPA-E-e2e-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
- Xet hash:
- f0fc93f95ca2d5d2e12e7c637043a262f074052f50785ad26f36fa05bb2626a5
- Size of remote file:
- 335 MB
- SHA256:
- 8e0500b82d85441aad7de1ae3dfcb7f5ff7b7901a411a2bb6185a7356515fc87
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