Instructions to use INCModel/Wan2.2-T2V-A14B-Diffusers-MXFP8-AutoRound with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use INCModel/Wan2.2-T2V-A14B-Diffusers-MXFP8-AutoRound with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("INCModel/Wan2.2-T2V-A14B-Diffusers-MXFP8-AutoRound", 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
| { | |
| "_class_name": "WanTransformer3DModel", | |
| "_diffusers_version": "0.37.1", | |
| "_name_or_path": "/dataset/Wan2.2-T2V-A14B-Diffusers/transformer", | |
| "added_kv_proj_dim": null, | |
| "attention_head_dim": 128, | |
| "cross_attn_norm": true, | |
| "eps": 1e-06, | |
| "ffn_dim": 13824, | |
| "freq_dim": 256, | |
| "image_dim": null, | |
| "in_channels": 16, | |
| "num_attention_heads": 40, | |
| "num_layers": 40, | |
| "out_channels": 16, | |
| "patch_size": [ | |
| 1, | |
| 2, | |
| 2 | |
| ], | |
| "pos_embed_seq_len": null, | |
| "qk_norm": "rms_norm_across_heads", | |
| "quantization_config": { | |
| "act_bits": 8, | |
| "act_data_type": "mx_fp", | |
| "act_dynamic": true, | |
| "act_group_size": 32, | |
| "act_sym": true, | |
| "autoround_version": "0.13.0", | |
| "bits": 8, | |
| "block_name_to_quantize": "blocks", | |
| "data_type": "mx_fp", | |
| "group_size": 32, | |
| "iters": 0, | |
| "packing_format": "auto_round:llm_compressor", | |
| "quant_method": "auto-round", | |
| "sym": true | |
| }, | |
| "rope_max_seq_len": 1024, | |
| "text_dim": 4096 | |
| } | |