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
File size: 566 Bytes
47ea602 | 1 2 3 4 5 6 7 8 9 | #!/usr/bin/env bash
# On the pod: after the first teacher run finishes, generate more teacher samples for other prompts
# (small shards so a stop loses little; train.py scans shards directly).
while pgrep -f "rapid_moss.teachergen --model" > /dev/null; do sleep 15; done
cd /workspace/code
torchrun --nproc-per-node 8 -m rapid_moss.teachergen --model /workspace/models/MOSS-Image-SFT --cache /workspace/cache \
--prompts /workspace/prompts/train.jsonl --out /workspace/teacher2 --count 4000 --seed 8 --shard-size 25 \
--exclude /workspace/teacher/index.json
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