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
File size: 807 Bytes
245cca0 | 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 | # 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
```json
{
"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
}
```
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