Instructions to use ivanmikhnenkov/tinydit-256 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use ivanmikhnenkov/tinydit-256 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("ivanmikhnenkov/tinydit-256", 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
File size: 703 Bytes
def994c | 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 | {
"architecture": "TinyDiT",
"latent_ch": 32,
"ctx_dim": 768,
"patch": 2,
"n_registers": 16,
"register_block": 3,
"n_null": 2,
"dim": 896,
"depth": 16,
"heads": 14,
"autoencoder": "black-forest-labs/FLUX.2-dev (vae subfolder)",
"text_encoder": "google/flan-t5-base",
"max_tokens_long": 128,
"max_tokens_short": 48,
"latent_whitening": "stats.json (per-channel mean/std, applied after encode and undone before decode)",
"sampler": {
"steps": 20,
"cfg": 4.0,
"shift": 2.8,
"solver": "euler"
},
"training_shapes": [
[
256,
256
],
[
288,
224
],
[
224,
288
],
[
320,
208
],
[
208,
320
]
],
"step": 400000,
"ema": "0.9999, bf16"
} |