Instructions to use b-f/ContinuitySeq-A with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use b-f/ContinuitySeq-A with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("b-f/ContinuitySeq-A", 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
Variable length frame conditioning for infinite length video generation. Can be used to generate videos from text that continue from an existing video or image. It can also generate the initial image using a standard text-to-image model from huggingface. Developed in collaboration with motexture and based on vseq2vseq.
Repo for inference can be found at ContinuitySeq.
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