Instructions to use TomLjm/MUGen-VR-AnyFlow-Conditioner with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use TomLjm/MUGen-VR-AnyFlow-Conditioner 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("TomLjm/MUGen-VR-AnyFlow-Conditioner", 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
| { | |
| "schema_version": 1, | |
| "title": "Targeted Consistency Evaluation", | |
| "protocol": { | |
| "samples": 40, | |
| "generation_seed": 42, | |
| "backbone": "frozen AnyFlow-FAR 1.3B", | |
| "condition_scale": 0.05 | |
| }, | |
| "metrics": [ | |
| { | |
| "name": "subject_consistency", | |
| "baseline": 0.8832832113063583, | |
| "mugen": 0.8859599555454527, | |
| "absolute_change": 0.002676744239094364, | |
| "relative_error_reduction": 0.022933669346577767 | |
| }, | |
| { | |
| "name": "motion_smoothness", | |
| "baseline": 0.9820145276399405, | |
| "mugen": 0.9824786442015501, | |
| "absolute_change": 0.00046411656160960657 | |
| }, | |
| { | |
| "name": "temporal_flickering", | |
| "baseline": 0.9696340364802118, | |
| "mugen": 0.9697767750305288, | |
| "absolute_change": 0.0001427385503169898 | |
| } | |
| ] | |
| } |