Instructions to use kandinskylab/tiny-kandinsky-s2v-modular-pipe with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use kandinskylab/tiny-kandinsky-s2v-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("kandinskylab/tiny-kandinsky-s2v-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
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Download README.md from kandinskylab/tiny-kandinsky-s2v-modular-pipe: direct link, hf CLI and curl.
- Browser
- Download file 359 Bytes
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https://huggingface.co/kandinskylab/tiny-kandinsky-s2v-modular-pipe/resolve/main/README.md
- Command line
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hf download hf://kandinskylab/tiny-kandinsky-s2v-modular-pipe/README.md
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curl -L -o README.md https://huggingface.co/kandinskylab/tiny-kandinsky-s2v-modular-pipe/resolve/main/README.md
359 Bytes
metadata
library_name: diffusers
tags:
- test
Tiny Kandinsky S2V 1.0 (random weights, for tests)
Randomly initialised, tiny versions of every Kandinsky S2V 1.0 component. It generates noise, not video: it exists only so the Diffusers modular pipeline tests can load a complete pipeline in seconds. For the real models see the Kandinsky S2V 1.0 collection.