Instructions to use ZhengmingYu/DMAD with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use ZhengmingYu/DMAD with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline from diffusers.utils import export_to_video # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("MiniMaxAI/MiniMax-H3", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("ZhengmingYu/DMAD") prompt = "A man with short gray hair plays a red electric guitar." output = pipe(prompt=prompt).frames[0] export_to_video(output, "output.mp4") - Inference
- Notebooks
- Google Colab
- Kaggle
new 8-step worse than 4 step in motion
#4
by mynhtn - opened
So I've tried both workflows on same seeds on some of my previous generations from 4 step full critic. I can say the colors are different - better in 8 step version, but the 8-step LoRA is completely unsuitable for scenes involving motion. When fingers move, a 4-step LoRA produces a natural motion blur, whereas an 8-step LoRA results in strange, jagged-looking fingers. All other settings remained identical in my A/B tests. Inference was set to 10 steps.