File size: 4,944 Bytes
b67e8f3 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 | ---
license: mit
library_name: diffusers
pipeline_tag: text-to-image
tags:
- diffusers
- minit2i
- image-generation
- text-to-image
- flow-matching
- pixel-space
inference: true
widget:
- text: A lonely astronaut standing on a quiet beach under two moons.
output:
url: MiniT2I-B-16/demo.png
language:
- en
---
# BiliSakura/MiniT2I-diffusers
Self-contained MiniT2I text-to-image checkpoints for Hugging Face diffusers. Each variant folder ships its own pipeline code, component modules, bundled FLAN-T5-Large text encoder, and transformer weights.
Converted from [`MiniT2I/MiniT2I`](https://huggingface.co/MiniT2I/MiniT2I) using [MiniT2I-diffusers](https://github.com/Bili-Sakura/Visual-Generative-Foundation-Model-Collection/tree/main/libs/MiniT2I-diffusers) in [Visual-Generative-Foundation-Model-Collection](https://github.com/Bili-Sakura/Visual-Generative-Foundation-Model-Collection).
## Available checkpoints
| Subfolder | Model | Params (denoiser + text encoder) | Patch | Recommended CFG |
| --- | --- | --- | ---: | ---: |
| [`MiniT2I-B-16/`](MiniT2I-B-16/) | MiniT2I-B/16 | 258M + 341M | 16 | 2.5 |
| [`MiniT2I-L-16/`](MiniT2I-L-16/) | MiniT2I-L/16 | 912M + 341M | 16 | 6.0 |
## Repo layout
```text
BiliSakura/MiniT2I-diffusers/
├── README.md
├── MiniT2I-B-16/
│ ├── pipeline.py
│ ├── model_index.json
│ ├── conversion_metadata.json
│ ├── demo.png
│ ├── scheduler/
│ │ └── scheduler_config.json
│ ├── text_encoder/
│ ├── tokenizer/
│ └── transformer/
│ ├── config.json
│ ├── diffusion_pytorch_model.safetensors
│ └── transformer_minit2i.py
└── MiniT2I-L-16/
└── ...
```
Each variant is self-contained: load with `custom_pipeline=.../pipeline.py` and `trust_remote_code=True`. MiniT2I denoises directly in RGB pixel space (no VAE).
## Demo
Prompt: *"A lonely astronaut standing on a quiet beach under two moons."* — MiniT2I-B/16 at 512×512, 100 steps, `guidance_scale=2.5`, seed 42.
## Load from Hugging Face
```python
import torch
from diffusers import DiffusionPipeline
pipe = DiffusionPipeline.from_pretrained(
"BiliSakura/MiniT2I-diffusers/MiniT2I-B-16",
trust_remote_code=True,
torch_dtype=torch.bfloat16,
).to("cuda")
generator = torch.Generator(device="cuda").manual_seed(42)
image = pipe(
"A lonely astronaut standing on a quiet beach under two moons.",
num_inference_steps=100,
guidance_scale=2.5,
generator=generator,
).images[0]
image.save("demo.png")
```
For MiniT2I-L/16, use `MiniT2I-L-16` and `guidance_scale=6.0`.
## Load from a local clone
```python
from pathlib import Path
import torch
from diffusers import DiffusionPipeline
model_dir = Path("./MiniT2I-B-16").resolve()
pipe = DiffusionPipeline.from_pretrained(
str(model_dir),
local_files_only=True,
custom_pipeline=str(model_dir / "pipeline.py"),
trust_remote_code=True,
torch_dtype=torch.bfloat16,
).to("cuda")
generator = torch.Generator(device="cuda").manual_seed(42)
image = pipe(
"A lonely astronaut standing on a quiet beach under two moons.",
num_inference_steps=100,
guidance_scale=2.5,
generator=generator,
).images[0]
image.save("demo.png")
```
Load a **variant subfolder** (e.g. `./MiniT2I-B-16`), not the repo root.
## Recommended inference settings
| Variant | Resolution | Steps | CFG scale | `torch_dtype` |
| --- | --- | ---: | ---: | --- |
| `MiniT2I-B-16` | 512×512 | 100 | 2.5 | `bfloat16` |
| `MiniT2I-L-16` | 512×512 | 100 | 6.0 | `bfloat16` |
For GenEval / DPG-Bench evaluation, upstream configs use `guidance_scale=5.0` for both B/16 and L/16.
## Interface notes
- Text conditioning uses bundled `google/flan-t5-large` (`T5EncoderModel` + `T5Tokenizer`).
- Scheduler is `FlowMatchEulerDiscreteScheduler` with 1000 training timesteps and `shift=1.0`.
- `guidance_scale > 1.0` enables classifier-free guidance with an empty-string null prompt.
- Output resolution is fixed at 512×512 for these exports.
## Regenerate bundles
From the repository root:
```bash
conda activate rsgen
python scripts/convert_minit2i_to_bilisakura.py
```
## Links
- Blog: [MiniT2I: A Minimalist Baseline for Text-to-Image Generation](https://peppaking8.github.io/#/post/minit2i)
- Upstream checkpoints: [MiniT2I/MiniT2I](https://huggingface.co/MiniT2I/MiniT2I)
- PyTorch/Diffusers source: [MiniT2I-diffusers](https://github.com/Bili-Sakura/Visual-Generative-Foundation-Model-Collection/tree/main/libs/MiniT2I-diffusers)
## Citation
```bibtex
@misc{minit2i2026,
title = {MiniT2I: A Minimalist Baseline for Text-to-Image Generation},
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}
}
```
|