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 | |
| ```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 | |
| } | |
| ``` | |