Instructions to use Motif-Technologies/Motif-Video-2B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Motif-Technologies/Motif-Video-2B with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("Motif-Technologies/Motif-Video-2B", dtype=torch.bfloat16, device_map="cuda") prompt = "A vibrant blue jay perches gracefully on a slender branch, its feathers shimmering in the soft morning light. The bird's keen eyes scan the surroundings, capturing the essence of the tranquil forest. It flutters its wings briefly, showcasing the intricate patterns of blue, white, and black on its plumage. The background reveals a lush canopy of green leaves, with rays of sunlight filtering through, creating a dappled effect on the forest floor. The blue jay then tilts its head, emitting a melodious call that echoes through the serene woodland, adding a touch of magic to the peaceful scene." image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
Can MoE be used to get this to 4B and still fit into 5090 VRAM?
(I am not an expert so this question may seem a bit silly.)
Is it possible to use the split (high noise and low noise) approach that Wan 2.2 uses with your architecture so that it could be a 4B model instead of only 2B?
Since the 5090 is the only relatively affordable option and is still limited to 32 GB, it seems that anything that can take full advantage of that (without requiring swapping to system RAM for the model) is the best way to go. Text encoding seems to be able to be done with a CPU without that causing much slowdown.
If the text encoder is swapped to system RAM and run from the CPU, could this be even larger than 4B if MoE is used?
Hi @usernameSRSalreadyexists ,
Good question! The current 2B model doesn't support MoE, but we are planning to explore MoE for our next model. Stay tuned!