Instructions to use hf-internal-testing/tiny-AudioLDM2Pipeline with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use hf-internal-testing/tiny-AudioLDM2Pipeline with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("hf-internal-testing/tiny-AudioLDM2Pipeline", 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
File size: 803 Bytes
c92b425 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 | {
"_class_name": "AudioLDM2Pipeline",
"_diffusers_version": "0.39.0.dev0",
"feature_extractor": [
"transformers",
"ClapFeatureExtractor"
],
"language_model": [
"transformers",
"GPT2LMHeadModel"
],
"projection_model": [
"diffusers",
"AudioLDM2ProjectionModel"
],
"scheduler": [
"diffusers",
"DDIMScheduler"
],
"text_encoder": [
"transformers",
"ClapModel"
],
"text_encoder_2": [
"transformers",
"T5EncoderModel"
],
"tokenizer": [
"transformers",
"RobertaTokenizer"
],
"tokenizer_2": [
"transformers",
"T5Tokenizer"
],
"unet": [
"diffusers",
"AudioLDM2UNet2DConditionModel"
],
"vae": [
"diffusers",
"AutoencoderKL"
],
"vocoder": [
"transformers",
"SpeechT5HifiGan"
]
}
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