Instructions to use thuml/rt1-compressive-tokenizer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use thuml/rt1-compressive-tokenizer with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("thuml/rt1-compressive-tokenizer", 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: 844 Bytes
57bf23d | 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 | {
"_class_name": "CompressiveVQModelFSQ",
"_diffusers_version": "0.27.0.dev0",
"act_fn": "silu",
"block_out_channels": [
128,
256,
256,
512
],
"context_length": 1,
"down_block_types": [
"DownEncoderBlock2D",
"DownEncoderBlock2D",
"DownEncoderBlock2D",
"DownEncoderBlock2D"
],
"dyn_fsq_levels": 12,
"force_upcast": true,
"in_channels": 3,
"latent_channels": 64,
"layers_per_block": 2,
"lookup_from_codebook": true,
"max_att_resolution": 32,
"mid_block_add_attention": false,
"norm_num_groups": 32,
"norm_type": "group",
"out_channels": 3,
"patch_size": 4,
"resolution": 256,
"sample_size": 32,
"scaling_factor": 0.18215,
"up_block_types": [
"UpDecoderBlock2D",
"UpDecoderBlock2D",
"UpDecoderBlock2D",
"UpDecoderBlock2D"
],
"vq_fsq_levels": 12
}
|