Text-to-Audio
Diffusers
Safetensors
ACE-Step
AceStepPipeline
audio
music
text-to-music
flow-matching
Instructions to use ACE-Step/acestep-v15-xl-turbo-diffusers with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use ACE-Step/acestep-v15-xl-turbo-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("ACE-Step/acestep-v15-xl-turbo-diffusers", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - ACE-Step
How to use ACE-Step/acestep-v15-xl-turbo-diffusers with ACE-Step:
# No code snippets available yet for this library. # To use this model, check the repository files and the library's documentation. # Want to help? PRs adding snippets are welcome at: # https://github.com/huggingface/huggingface.js
- Notebooks
- Google Colab
- Kaggle
File size: 491 Bytes
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"_class_name": "AceStepPipeline",
"_diffusers_version": "0.38.0.dev0",
"condition_encoder": [
"ace_step",
"AceStepConditionEncoder"
],
"scheduler": [
"diffusers",
"FlowMatchEulerDiscreteScheduler"
],
"text_encoder": [
"transformers",
"Qwen3Model"
],
"tokenizer": [
"transformers",
"Qwen2TokenizerFast"
],
"transformer": [
"diffusers",
"AceStepTransformer1DModel"
],
"vae": [
"diffusers",
"AutoencoderOobleck"
]
}
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