Instructions to use cuio/MiniT2I with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use cuio/MiniT2I with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("cuio/MiniT2I", 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
| license: mit | |
| pipeline_tag: text-to-image | |
| library_name: diffusers | |
| private: true | |
| tags: | |
| - text-to-image | |
| - diffusers | |
| - pytorch | |
| - minit2i | |
| # MiniT2I Diffusers Checkpoints | |
| This private repository contains the Diffusers-compatible PyTorch weights for both MiniT2I-B/16 and MiniT2I-L/16. MiniT2I-B/16 uses the JAX checkpoint EMA decay `0.99995`, and MiniT2I-L/16 uses EMA decay `0.9999`; both are exported from step 290K. Load one repository, then select the model at inference time with `model_type`. | |
| ## Models | |
| | `model_type` | Model | Directory | | |
| | --- | --- | --- | | |
| | `b16` | MiniT2I-B/16 | `minit2i-b-16/` | | |
| | `l16` | MiniT2I-L/16 | `minit2i-l-16/` | | |
| Aliases such as `b`, `base`, `minit2i-b/16`, `l`, `large`, and `minit2i-l/16` are also supported. | |
| ## Usage | |
| ```python | |
| import torch | |
| from diffusers import DiffusionPipeline | |
| HUB_MODEL_ID = "MiniT2I/MiniT2I" | |
| pipe = DiffusionPipeline.from_pretrained( | |
| HUB_MODEL_ID, | |
| custom_pipeline=HUB_MODEL_ID, | |
| trust_remote_code=True, | |
| ) | |
| image = pipe( | |
| "A lonely astronaut standing on a quiet beach under two moons.", | |
| model_type="b16", | |
| guidance_scale=2.5, | |
| num_inference_steps=100, | |
| torch_dtype=torch.bfloat16, | |
| ).images[0] | |
| image.save("minit2i-b16.png") | |
| image = pipe( | |
| "a watercolor painting of a mountain lake at sunrise", | |
| model_type="l16", | |
| guidance_scale=6.0, | |
| num_inference_steps=100, | |
| torch_dtype=torch.bfloat16, | |
| ).images[0] | |
| image.save("minit2i-l16.png") | |
| ``` | |
| The selected submodel is downloaded lazily from this repository, so calling with `model_type="b16"` does not download the L/16 weights. | |
| ## Links | |
| - Blog: [Text-to-Image Generation Made Simple](https://peppaking8.github.io/#/post/text-to-image-generation-made-simple) | |
| - PyTorch/Diffusers release: [Hope7Happiness/t2i-release](https://github.com/Hope7Happiness/t2i-release) | |
| - JAX release: [PeppaKing8/minit2i-jax](https://github.com/PeppaKing8/minit2i-jax) | |
| ## Related Checkpoints | |
| Original JAX checkpoints are stored separately in private repositories: | |
| - `MiniT2I/MiniT2I-B-16-jax` for MiniT2I-B/16 | |
| - `MiniT2I/MiniT2I-L-16-jax` for MiniT2I-L/16 | |
| ## Citation | |
| ```bibtex | |
| @misc{minit2i2026, | |
| title = {MiniT2I: A Minimalist Baseline for Text-to-Image Synthesis}, | |
| author = {Wang, Xianbang and Zhao, Hanhong and Lu, Yiyang and Zhou, Kangyang and Ma, Linrui and He, Kaiming}, | |
| year = {2026}, | |
| url = {https://peppaking8.github.io/#/post/minit2i} | |
| } | |
| ``` | |