Instructions to use WaveCut/MiniMax-H3-Image-Pruned with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use WaveCut/MiniMax-H3-Image-Pruned with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("WaveCut/MiniMax-H3-Image-Pruned", 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
- Local Apps Settings
- Draw Things
- DiffusionBee
Download transformer/build_report.json from WaveCut/MiniMax-H3-Image-Pruned: direct link, hf CLI and curl.
- Browser
- Download file 1.7 kB
-
https://huggingface.co/WaveCut/MiniMax-H3-Image-Pruned/resolve/main/transformer/build_report.json
- Command line
-
hf download hf://WaveCut/MiniMax-H3-Image-Pruned/transformer/build_report.json
-
curl -L -o build_report.json https://huggingface.co/WaveCut/MiniMax-H3-Image-Pruned/resolve/main/transformer/build_report.json
1.7 kB
| { | |
| "time_embed_hidden_dim": 16, | |
| "time_embed_dim": 8, | |
| "grid": [ | |
| 0.0, | |
| 0.03247630596160889, | |
| 0.11245149374008179, | |
| 0.25, | |
| 0.4672260880470276, | |
| 0.7679911255836487, | |
| 0.9990000128746033 | |
| ], | |
| "plans": [ | |
| { | |
| "timesteps": [ | |
| 0.0 | |
| ], | |
| "rows": 51, | |
| "identical_rows": 51, | |
| "differing": 0 | |
| }, | |
| { | |
| "timesteps": [ | |
| 0.03247630596160889 | |
| ], | |
| "rows": 51, | |
| "identical_rows": 51, | |
| "differing": 0 | |
| }, | |
| { | |
| "timesteps": [ | |
| 0.11245149374008179 | |
| ], | |
| "rows": 51, | |
| "identical_rows": 51, | |
| "differing": 0 | |
| }, | |
| { | |
| "timesteps": [ | |
| 0.25 | |
| ], | |
| "rows": 51, | |
| "identical_rows": 51, | |
| "differing": 0 | |
| }, | |
| { | |
| "timesteps": [ | |
| 0.4672260880470276 | |
| ], | |
| "rows": 51, | |
| "identical_rows": 51, | |
| "differing": 0 | |
| }, | |
| { | |
| "timesteps": [ | |
| 0.7679911255836487 | |
| ], | |
| "rows": 51, | |
| "identical_rows": 51, | |
| "differing": 0 | |
| }, | |
| { | |
| "timesteps": [ | |
| 0.0, | |
| 0.9990000128746033 | |
| ], | |
| "rows": 102, | |
| "identical_rows": 51, | |
| "differing": 3064 | |
| }, | |
| { | |
| "timesteps": [ | |
| 0.03247630596160889, | |
| 0.9990000128746033 | |
| ], | |
| "rows": 102, | |
| "identical_rows": 51, | |
| "differing": 19629 | |
| }, | |
| { | |
| "timesteps": [ | |
| 0.11245149374008179, | |
| 0.9990000128746033 | |
| ], | |
| "rows": 102, | |
| "identical_rows": 51, | |
| "differing": 3683 | |
| }, | |
| { | |
| "timesteps": [ | |
| 0.25, | |
| 0.9990000128746033 | |
| ], | |
| "rows": 102, | |
| "identical_rows": 51, | |
| "differing": 18412 | |
| }, | |
| { | |
| "timesteps": [ | |
| 0.4672260880470276, | |
| 0.9990000128746033 | |
| ], | |
| "rows": 102, | |
| "identical_rows": 51, | |
| "differing": 10318 | |
| }, | |
| { | |
| "timesteps": [ | |
| 0.7679911255836487, | |
| 0.9990000128746033 | |
| ], | |
| "rows": 102, | |
| "identical_rows": 51, | |
| "differing": 4173 | |
| } | |
| ] | |
| } | |