Any-to-Any
Transformers
Diffusers
Safetensors
English
llada2_moe
feature-extraction
multimodal
image-generation
image-understanding
image-editing
diffusion
Mixture of Experts
text-to-image
fp8
quantized
custom_code
Instructions to use inclusionAI/LLaDA2.0-Uni-FP8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use inclusionAI/LLaDA2.0-Uni-FP8 with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("inclusionAI/LLaDA2.0-Uni-FP8", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
File size: 500 Bytes
adc7c45 | 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 | {
"_class_name": "ZImageTransformer2DModel",
"_diffusers_version": "0.37.0.dev0",
"all_f_patch_size": [
1
],
"all_patch_size": [
2
],
"axes_dims": [
32,
48,
48
],
"axes_lens": [
1536,
512,
512
],
"cap_feat_dim": 4096,
"dim": 3840,
"in_channels": 16,
"n_heads": 30,
"n_kv_heads": 30,
"n_layers": 30,
"n_refiner_layers": 2,
"norm_eps": 1e-05,
"qk_norm": true,
"rope_theta": 256.0,
"siglip_feat_dim": null,
"t_scale": 1000.0
}
|