Text-to-Image
Diffusers
TensorBoard
StableDiffusionPipeline
stable-diffusion
stable-diffusion-diffusers
dreambooth
Instructions to use frknayk/dreambooth_training with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use frknayk/dreambooth_training with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("frknayk/dreambooth_training", dtype=torch.bfloat16, device_map="cuda") prompt = "a photo of sks dog" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
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Download diffusers/docs/source/en/api/pipelines/dance_diffusion.md from frknayk/dreambooth_training: direct link, hf CLI and curl.
- Browser
- Download file 1.48 kB
-
https://huggingface.co/frknayk/dreambooth_training/resolve/main/diffusers/docs/source/en/api/pipelines/dance_diffusion.md
- Command line
-
hf download hf://frknayk/dreambooth_training/diffusers/docs/source/en/api/pipelines/dance_diffusion.md
-
curl -L -o dance_diffusion.md https://huggingface.co/frknayk/dreambooth_training/resolve/main/diffusers/docs/source/en/api/pipelines/dance_diffusion.md
1.48 kB
Dance Diffusion
Dance Diffusion is by Zach Evans.
Dance Diffusion is the first in a suite of generative audio tools for producers and musicians released by Harmonai.
The original codebase of this implementation can be found at Harmonai-org.
Make sure to check out the Schedulers guide to learn how to explore the tradeoff between scheduler speed and quality, and see the reuse components across pipelines section to learn how to efficiently load the same components into multiple pipelines.
DanceDiffusionPipeline
[[autodoc]] DanceDiffusionPipeline - all - call
AudioPipelineOutput
[[autodoc]] pipelines.AudioPipelineOutput