Instructions to use MeissonFlow/Mei with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use MeissonFlow/Mei with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("MeissonFlow/Mei", 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
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Download README.md from MeissonFlow/Mei: direct link, hf CLI and curl.
- Browser
- Download file 513 Bytes
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https://huggingface.co/MeissonFlow/Mei/resolve/main/README.md
- Command line
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hf download hf://MeissonFlow/Mei/README.md
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curl -L -o README.md https://huggingface.co/MeissonFlow/Mei/resolve/main/README.md
513 Bytes
metadata
license: apache-2.0
library_name: diffusers
pipeline_tag: text-to-video
base_model:
- BAAI/URSA-1.7B-FSQ320
base_model_relation: finetune
DDMD-V: Distilling Uniform Discrete Diffusion Models into a One-Step Video Generator
The 3rd Generation MeissonFlow Foundation Model.
DDMD-V (Discrete Distribution Matching Distillation for Video) distills the uniform discrete video diffusion model URSA into a comparable one-step generator Mei.
More details can be found at https://github.com/viiika/DDMD-V