Instructions to use elvis92/pets_rank_2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use elvis92/pets_rank_2 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("elvis92/pets_rank_2") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" 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/audio_diffusion.md from elvis92/pets_rank_2: direct link, hf CLI and curl.
- Browser
- Download file 1.45 kB
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https://huggingface.co/elvis92/pets_rank_2/resolve/main/diffusers/docs/source/en/api/pipelines/audio_diffusion.md
- Command line
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hf download hf://elvis92/pets_rank_2/diffusers/docs/source/en/api/pipelines/audio_diffusion.md
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curl -L -o audio_diffusion.md https://huggingface.co/elvis92/pets_rank_2/resolve/main/diffusers/docs/source/en/api/pipelines/audio_diffusion.md
1.45 kB
Audio Diffusion
Audio Diffusion is by Robert Dargavel Smith, and it leverages the recent advances in image generation from diffusion models by converting audio samples to and from Mel spectrogram images.
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.
AudioDiffusionPipeline
[[autodoc]] AudioDiffusionPipeline - all - call
AudioPipelineOutput
[[autodoc]] pipelines.AudioPipelineOutput
ImagePipelineOutput
[[autodoc]] pipelines.ImagePipelineOutput
Mel
[[autodoc]] Mel