Instructions to use optimum-intel-internal-testing/tiny-random-ltx-video-0.9.1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use optimum-intel-internal-testing/tiny-random-ltx-video-0.9.1 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("optimum-intel-internal-testing/tiny-random-ltx-video-0.9.1", 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: 1,195 Bytes
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"_class_name": "AutoencoderKLLTXVideo",
"_diffusers_version": "0.33.0.dev0",
"block_out_channels": [
8,
8,
8,
8
],
"decoder_block_out_channels": [
8,
8,
8
],
"decoder_causal": false,
"decoder_inject_noise": [
false,
false,
false,
false
],
"decoder_layers_per_block": [
1,
1,
1,
1
],
"decoder_spatio_temporal_scaling": [
true,
true,
false
],
"down_block_types": [
"LTXVideoDownBlock3D",
"LTXVideoDownBlock3D",
"LTXVideoDownBlock3D",
"LTXVideoDownBlock3D"
],
"downsample_type": [
"conv",
"conv",
"conv",
"conv"
],
"encoder_causal": true,
"in_channels": 3,
"latent_channels": 8,
"layers_per_block": [
1,
1,
1,
1,
1
],
"out_channels": 3,
"patch_size": 1,
"patch_size_t": 1,
"resnet_norm_eps": 1e-06,
"scaling_factor": 1.0,
"spatial_compression_ratio": null,
"spatio_temporal_scaling": [
true,
true,
false,
false
],
"temporal_compression_ratio": null,
"timestep_conditioning": true,
"upsample_factor": [
1,
1,
1
],
"upsample_residual": [
false,
false,
false
]
}
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