Instructions to use memescreamer/ACE-Step-v1-3.5B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use memescreamer/ACE-Step-v1-3.5B with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("memescreamer/ACE-Step-v1-3.5B", 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 memescreamer/ACE-Step-v1-3.5B 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: 639 Bytes
070d866 | 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 | {
"_class_name": "ACEStepTransformer2DModel",
"_diffusers_version": "0.32.2",
"attention_head_dim": 128,
"in_channels": 8,
"inner_dim": 2560,
"lyric_encoder_vocab_size": 6693,
"lyric_hidden_size": 1024,
"max_height": 16,
"max_position": 32768,
"max_width": 32768,
"mlp_ratio": 2.5,
"num_attention_heads": 20,
"num_layers": 24,
"out_channels": 8,
"patch_size": [
16,
1
],
"rope_theta": 1000000.0,
"speaker_embedding_dim": 512,
"ssl_encoder_depths": [
8,
8
],
"ssl_latent_dims": [
1024,
768
],
"ssl_names": [
"mert",
"m-hubert"
],
"text_embedding_dim": 768
}
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