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/scripts/pod_stop_train.sh from darask0/MOSS-Image_4-step_adapter_lora: direct link, hf CLI and curl.
- Browser
- Download file 220 Bytes
-
https://huggingface.co/darask0/MOSS-Image_4-step_adapter_lora/resolve/main/code/scripts/pod_stop_train.sh
- Command line
-
hf download hf://darask0/MOSS-Image_4-step_adapter_lora/code/scripts/pod_stop_train.sh
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curl -L -o pod_stop_train.sh https://huggingface.co/darask0/MOSS-Image_4-step_adapter_lora/resolve/main/code/scripts/pod_stop_train.sh
220 Bytes
| # On the pod: stop the training run (torchrun + workers) and its pod_dmad.sh wrapper. | |
| pkill -f "scripts/pod_dmad.sh" | |
| pkill -f "rapid_moss.train" | |
| sleep 5 | |
| pgrep -fa "rapid_moss.train" | head -3 || true | |