Image-to-Video
Diffusers
Safetensors
Cosmos3OmniDiffusersPipeline
cosmos3_omni
nvidia
cosmos3
world-model
omnimodel
diffusion
text-to-image
text-to-video
quantized
modelopt
fp8
blackwell
Instructions to use prometheusAIR/Cosmos3-Super-FP8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use prometheusAIR/Cosmos3-Super-FP8 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline from diffusers.utils import load_image, export_to_video # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("prometheusAIR/Cosmos3-Super-FP8", dtype=torch.bfloat16, device_map="cuda") pipe.to("cuda") prompt = "A man with short gray hair plays a red electric guitar." image = load_image( "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/guitar-man.png" ) output = pipe(image=image, prompt=prompt).frames[0] export_to_video(output, "output.mp4") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6aec39639a0a2d1ca966356b8c2b8426a484f80ff80731f44fa8482040713bdf
- Size of remote file:
- 11.4 MB
- SHA256:
- aeb13307a71acd8fe81861d94ad54ab689df773318809eed3cbe794b4492dae4
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.