Instructions to use jia0160/Wan2.1-T2V-1.3B-Diffusers-MXFP4-AutoRoundFormat-rtn with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use jia0160/Wan2.1-T2V-1.3B-Diffusers-MXFP4-AutoRoundFormat-rtn with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("jia0160/Wan2.1-T2V-1.3B-Diffusers-MXFP4-AutoRoundFormat-rtn", 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
Wan2.1 MXFP4 quantization structure report
- Source:
/workspace/lj/models/Wan2.1-T2V-1.3B-Diffusers - Quantized:
/workspace/lj/models/Wan2.1-T2V-1.3B-Diffusers-MXFP4-AutoRoundFormat-rtn - Mode:
RTN(iters=0) - Quantized shards:
1 - Quantized transformer size:
0.739 GiB - Source transformer size:
5.286 GiB - Approximate size reduction:
7.15x
quantization_config
{
"act_bits": 4,
"act_data_type": "mx_fp",
"act_dynamic": true,
"act_group_size": 32,
"act_sym": true,
"autoround_version": "0.14.2",
"batch_size": 1,
"bits": 4,
"block_name_to_quantize": "blocks",
"data_type": "mx_fp",
"enable_quanted_input": false,
"group_size": 32,
"nsamples": 16,
"packing_format": "auto_round:llm_compressor",
"quant_method": "auto-round",
"sym": true
}