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
Commit History
Today
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Fix rapid-moss compatibility with native ComfyUI MOSS models
Verified signatureSupport fused QKV projections and preserve fp32 modulation/residuals in native blocks. Add native UI/API workflows and regression coverage. Validated 1024x1024, 4-step generation with native and HF loaders; 8 tests pass.1ec327b -
README: cautions (required settings, recommended settings, known weaknesses, untested areas)
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ComfyUI custom node (unmerged fp32 LoRA + fp32 time path + data-space sampler), example workflows, GPU check
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rapid-moss: MOSS-Image-SFT 4-step adapter (data-space DMD + DMAD, it 500 EMA), code, logs, eval
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initial commit
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