Instructions to use onkarsus13/UniDFlow-A2A with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use onkarsus13/UniDFlow-A2A with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("onkarsus13/UniDFlow-A2A", 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
| { | |
| "batch_size": 2, | |
| "accum_iter": 4, | |
| "epochs": 2, | |
| "warmup_epochs": 0.001, | |
| "lr": 2e-05, | |
| "min_lr": 2e-05, | |
| "wd": 0.1, | |
| "clip_grad": 4.0, | |
| "init_from": "", | |
| "data_config": "", | |
| "cache_ann_on_disk": true, | |
| "length_clustering": true, | |
| "num_workers": 16, | |
| "pin_mem": true, | |
| "seed": 0, | |
| "output_dir": "", | |
| "save_interval": 1, | |
| "save_iteration_interval": 200, | |
| "only_save_trainable": false, | |
| "ckpt_max_keep": 2, | |
| "auto_resume": true, | |
| "resume_path": null, | |
| "model_parallel_size": 1, | |
| "data_parallel": "sdp", | |
| "precision": "bf16", | |
| "grad_precision": "fp32", | |
| "checkpointing": false, | |
| "max_seq_len": 5120, | |
| "mask_image_logits": false, | |
| "dropout": 0.05, | |
| "z_loss_weight": 1e-05, | |
| "model_size": "7B", | |
| "world_size": 48, | |
| "rank": 0, | |
| "gpu": 0, | |
| "local_rank": 0, | |
| "dist_url": "env://", | |
| "distributed": true, | |
| "dist_backend": "nccl" | |
| } |