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: 1,086 Bytes
47ea602 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 | #!/usr/bin/env bash
# Runs on the pod: Python deps + model downloads into /workspace (idempotent).
set -euo pipefail
cd /workspace
export HF_HUB_ENABLE_HF_TRANSFER=0 HF_XET_HIGH_PERFORMANCE=1 PIP_BREAK_SYSTEM_PACKAGES=1
pip install -q --root-user-action=ignore "diffusers==0.38.0" "transformers==5.17.0" "accelerate>=1.10" \
"safetensors>=0.6" "huggingface_hub[hf_xet]>=1.0" pillow flash-linear-attention 2>&1 | tail -3
python - <<'EOF'
import torch, diffusers, transformers
print('torch', torch.__version__, 'cuda', torch.version.cuda, 'diffusers', diffusers.__version__,
'transformers', transformers.__version__, [torch.cuda.get_device_name(i) for i in range(torch.cuda.device_count())])
EOF
mkdir -p models
hf download OpenMOSS-Team/MOSS-Image-SFT --local-dir models/MOSS-Image-SFT --exclude "assets/*" > /dev/null
hf download lodestones/MOSS-Image-SFT-8-steps-dmd lora.safetensors time_embedding.safetensors dataspace.safetensors README.md \
--local-dir models/MOSS-Image-SFT-8-steps-dmd > /dev/null
du -sh models/*
md5sum models/MOSS-Image-SFT/pipeline_moss_image.py
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