Instructions to use darask0/MOSS-Image_4-step_adapter_lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use darask0/MOSS-Image_4-step_adapter_lora with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("OpenMOSS-Team/MOSS-Image-SFT", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("darask0/MOSS-Image_4-step_adapter_lora") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
Download code/configs/dmad.json from darask0/MOSS-Image_4-step_adapter_lora: direct link, hf CLI and curl.
- Browser
- Download file 360 Bytes
-
https://huggingface.co/darask0/MOSS-Image_4-step_adapter_lora/resolve/main/code/configs/dmad.json
- Command line
-
hf download hf://darask0/MOSS-Image_4-step_adapter_lora/code/configs/dmad.json
-
curl -L -o dmad.json https://huggingface.co/darask0/MOSS-Image_4-step_adapter_lora/resolve/main/code/configs/dmad.json
360 Bytes
| { | |
| "method": "dmad", | |
| "out_dir": "/workspace/runs/dmad", | |
| "teacher_dir": ["/workspace/teacher", "/workspace/teacher2"], | |
| "iterations": 2500, | |
| "micro": 2, | |
| "critic_warmup": 50, | |
| "lr_gen": 2e-5, "lr_time": 2e-5, "lr_q": 2e-5, | |
| "lr_critic": 4e-5, | |
| "ema": 0.998, | |
| "log_every": 10, | |
| "sample_every": 100, | |
| "save_every": 200, | |
| "export_every": 500, | |
| "eval_count": 8 | |
| } | |