Instructions to use omnimem/self_forcing_from_ode_init with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use omnimem/self_forcing_from_ode_init with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("omnimem/self_forcing_from_ode_init", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - SelfForcing
How to use omnimem/self_forcing_from_ode_init with SelfForcing:
# No code snippets available yet for this library. # To use this model, check the repository files and the library's documentation. # Want to help? PRs adding snippets are welcome at: # https://github.com/huggingface/huggingface.js
- Notebooks
- Google Colab
- Kaggle
File size: 696 Bytes
3b8f9ea | 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 | {
"_class_name": "FSDPCausalWanNSAModel",
"_diffusers_version": "0.37.1",
"_name_or_path": "/mnt/localssd/omnimem-sf-t12-ode-h100-p2-20260926/assets/model_path/ode-step800-model",
"chunk_length": 4680,
"dim": 1536,
"enable_nsa": true,
"eps": 1e-06,
"exclude_sink_chunks": 0,
"exclude_window_chunks": 3,
"ffn_dim": 8960,
"freq_dim": 256,
"gate_mode": "sigmoid",
"group_q_size": 15,
"in_dim": 16,
"model_type": "t2v",
"num_heads": 12,
"num_kv_head_groups": null,
"num_layers": 30,
"num_selected_blocks": 12,
"out_dim": 16,
"pool_f": 1,
"pool_h": 15,
"pool_w": 2,
"progressive_exclude": true,
"skip_module": null,
"text_len": 512,
"tf": false
}
|