Instructions to use Shriramnag/ShivAI-Image-to-Video with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Shriramnag/ShivAI-Image-to-Video with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("Shriramnag/ShivAI-Image-to-Video", 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: 971 Bytes
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name = "ltx-core"
version = "1.1.3"
description = "Core implementation of Lightricks' LTX-2 model"
readme = "README.md"
requires-python = ">=3.10"
dependencies = [
"torch~=2.7",
"torchaudio",
"einops",
"numpy",
"transformers>=4.52",
"safetensors",
"accelerate",
"scipy>=1.14",
]
[project.optional-dependencies]
xformers = ["xformers"]
fp8-trtllm = [
"tensorrt-llm==1.0.0",
"onnx>=1.16.0,<1.20.0",
"openmpi",
]
[tool.uv]
conflicts = [
[
{ extra = "xformers" },
{ extra = "fp8-trtllm" },
],
]
[tool.uv.sources]
xformers = { index = "pytorch" }
tensorrt-llm = { index = "nvidia" }
[[tool.uv.index]]
name = "pytorch"
url = "https://download.pytorch.org/whl/cu129"
explicit = true
[[tool.uv.index]]
name = "nvidia"
url = "https://pypi.nvidia.com/"
explicit = true
[build-system]
requires = ["uv_build>=0.9.8,<0.10.0"]
build-backend = "uv_build"
[dependency-groups]
dev = [
"scikit-image>=0.25.2",
]
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