Text-to-Video
Diffusers
Safetensors
LongLive2Pipeline
sglang
longlive
autoregressive
video-generation
Instructions to use Rabinovich/LongLive-2.0-5B-Diffusers with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Rabinovich/LongLive-2.0-5B-Diffusers with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("Rabinovich/LongLive-2.0-5B-Diffusers", torch_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: 511 Bytes
ace4831 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 | {
"_class_name": "LongLive2Pipeline",
"_diffusers_version": "0.35.0.dev0",
"boundary_ratio": null,
"expand_timesteps": true,
"scheduler": [
"diffusers",
"UniPCMultistepScheduler"
],
"text_encoder": [
"transformers",
"UMT5EncoderModel"
],
"tokenizer": [
"transformers",
"T5TokenizerFast"
],
"transformer": [
"diffusers",
"LongLive2Transformer3DModel"
],
"transformer_2": [
null,
null
],
"vae": [
"diffusers",
"AutoencoderKLWan"
]
}
|