Instructions to use TensorStack/AutoEncoder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use TensorStack/AutoEncoder with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("TensorStack/AutoEncoder", 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
Upload 2 files
Browse files
AceStepXL/config.json
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{
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"_class_name": "AutoencoderOobleck",
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"_diffusers_version": "0.38.0.dev0",
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"_name_or_path": "/vepfs-d-data/q-ace/repo/gongjunmin/ACE-Step-1.5/checkpoints/vae",
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"audio_channels": 2,
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"channel_multiples": [
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],
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"decoder_channels": 128,
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"decoder_input_channels": 64,
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"downsampling_ratios": [
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],
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"encoder_hidden_size": 128,
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"sampling_rate": 48000
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}
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AceStepXL/diffusion_pytorch_model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:da17edb604c40deaf09e9b24974e590d1ca83a374070e5d0884cfa4bed9a99b0
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size 337431388
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