Instructions to use Intel/Wan2.2-I2V-A14B-Diffusers-int4-AutoRound with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Intel/Wan2.2-I2V-A14B-Diffusers-int4-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("Intel/Wan2.2-I2V-A14B-Diffusers-int4-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
File size: 966 Bytes
0904698 | 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 42 | {
"_class_name": "WanTransformer3DModel",
"_diffusers_version": "0.37.0.dev0",
"_name_or_path": "/mnt/disk0/lvl/Wan2.2-I2V-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": 36,
"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": {
"autoround_version": "0.13.0",
"batch_size": 1,
"bits": 4,
"block_name_to_quantize": "blocks",
"data_type": "int",
"group_size": 128,
"iters": 100,
"low_gpu_mem_usage": true,
"lr": 0.005,
"minmax_lr": 0.005,
"nsamples": 32,
"packing_format": "auto_round:auto_gptq",
"quant_method": "auto-round",
"sym": true
},
"rope_max_seq_len": 1024,
"text_dim": 4096
}
|