Instructions to use akshan-main/tiny-ltx-modular-pipe with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use akshan-main/tiny-ltx-modular-pipe with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("akshan-main/tiny-ltx-modular-pipe", 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 vae/config.json with huggingface_hub
Browse files- vae/config.json +3 -3
vae/config.json
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"patch_size_t": 1,
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"resnet_norm_eps": 1e-06,
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"scaling_factor": 1.0,
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"spatial_compression_ratio":
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"spatio_temporal_scaling": [
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true
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],
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"temporal_compression_ratio":
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"timestep_conditioning": false,
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"upsample_factor": [
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1
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"upsample_residual": [
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false
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]
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}
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"patch_size_t": 1,
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"resnet_norm_eps": 1e-06,
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"scaling_factor": 1.0,
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"spatial_compression_ratio": 2,
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"spatio_temporal_scaling": [
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true
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],
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"temporal_compression_ratio": 2,
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"timestep_conditioning": false,
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"upsample_factor": [
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1
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"upsample_residual": [
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false
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]
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}
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