Image-to-Text
Diffusers
Safetensors
English
Non-Autoregressive
Masked-Generative-Transformer
Discrete-Diffusion
Unified-Model
Instructions to use MeissonFlow/Muddit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use MeissonFlow/Muddit 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/Muddit", 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
- Xet hash:
- 60a9e9aab58f5e2d1ee38a4db1bc41156bf4a9b6fb21f123b5bd37db9eb1549e
- Size of remote file:
- 4.23 GB
- SHA256:
- a8acdb7b1bc5a6cf7f0fcb6b7ad37ad09d0f9b3750fc34deb18e9b072a688009
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.