Instructions to use hf-internal-testing/tiny-LTX2Pipeline with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use hf-internal-testing/tiny-LTX2Pipeline 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-LTX2Pipeline", 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: 487 Bytes
f0dc9e2 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 | {
"_class_name": "AutoencoderKLLTX2Audio",
"_diffusers_version": "0.39.0.dev0",
"attn_resolutions": null,
"base_channels": 4,
"causality_axis": "height",
"ch_mult": [
1
],
"double_z": true,
"dropout": 0.0,
"in_channels": 2,
"is_causal": true,
"latent_channels": 2,
"mel_bins": 8,
"mel_hop_length": 160,
"mid_block_add_attention": false,
"norm_type": "pixel",
"num_res_blocks": 1,
"output_channels": 2,
"resolution": 32,
"sample_rate": 16000
}
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