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: 785 Bytes
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"_name_or_path": "hf-internal-testing/tiny-random-t5",
"architectures": [
"T5EncoderModel"
],
"bos_token_id": 0,
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"d_ff": 37,
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"decoder_start_token_id": 0,
"dense_act_fn": "relu",
"dropout_rate": 0.1,
"eos_token_id": 1,
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"gradient_checkpointing": false,
"initializer_factor": 0.002,
"is_encoder_decoder": true,
"is_gated_act": false,
"layer_norm_epsilon": 1e-06,
"model_type": "t5",
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"relative_attention_max_distance": 128,
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"torch_dtype": "float32",
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"use_cache": true,
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}
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