Instructions to use lodestones/Kromoss with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use lodestones/Kromoss with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("lodestones/Kromoss", dtype=torch.bfloat16, device_map="cuda") 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
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Kromoss is a full fine-tune of [MOSS-Image-Base](https://huggingface.co/OpenMOSS-Team/MOSS-Image-Base) at
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1024 px. It uses the same architecture, inference pipeline, text encoder (Qwen3.5-9B) and VAE (FLUX.2 small
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decoder) as [MOSS-Image-SFT](https://huggingface.co/OpenMOSS-Team/MOSS-Image-SFT), and this repo has the same
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layout, so it is a drop-in replacement.
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The repo also ships `8-steps-dmd-lora.safetensors`, an 8-step turbo LoRA (data-space DMD distillation of
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MOSS-Image-Base): **8 steps, CFG 1**, no negative branch.
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Kromoss is a full fine-tune of [MOSS-Image-Base](https://huggingface.co/OpenMOSS-Team/MOSS-Image-Base) at
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1024 px. It uses the same architecture, inference pipeline, text encoder (Qwen3.5-9B) and VAE (FLUX.2 small
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decoder) as [MOSS-Image-SFT](https://huggingface.co/OpenMOSS-Team/MOSS-Image-SFT), and this repo has the same
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layout, so it is a drop-in replacement.
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The repo also ships `8-steps-dmd-lora.safetensors`, an 8-step turbo LoRA (data-space DMD distillation of
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MOSS-Image-Base): **8 steps, CFG 1**, no negative branch.
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