Image-to-Text
Diffusers
Safetensors
English
uniar
image-generation
image-understanding
image-editing
multimodal
autoregressive
text-to-image
unified-model
Instructions to use ShareLab-SII/UniAR-RL with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use ShareLab-SII/UniAR-RL with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("ShareLab-SII/UniAR-RL", 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
File size: 648 Bytes
05d7bab | 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 | {
"depth": 27,
"hidden_size": 1152,
"hidden_act": "gelu_pytorch_tanh",
"intermediate_size": 4304,
"num_heads": 16,
"in_channels": 3,
"patch_size": 16,
"spatial_merge_size": 2,
"temporal_patch_size": 2,
"out_hidden_size": 4096,
"initializer_range": 0.02,
"use_bsq": true,
"bsq_dim": 64,
"bsq_hidden_dim": 8192,
"bsq_skip_final_layernorm": true,
"vistok_pred": false,
"vistok_pred_layernorm": false,
"vistok_pred_transformer_head": false,
"architectures": [
"UniARVisionModel"
],
"model_type": "uniar_vision",
"deepstack_visual_indexes": [
8,
16,
24
],
"num_position_embeddings": 2304
} |