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New 8-step LoRA for MOSS-Image-Base reduces inference steps

By PulseAugur Editorial · [1 sources] ·

A new 8-step LoRA (Low-Rank Adaptation) model has been developed for MOSS-Image-Base, a diffusion model. This LoRA, trained using a distribution matching technique, significantly reduces the number of inference steps required from 50 to just 8, while maintaining or improving realism. The LoRA specifically adapts the transformer component of the model, leaving the text encoder, VAE, tokenizer, and inference pipeline unchanged. AI

IMPACT This development offers a more efficient way to generate images with MOSS-Image-Base, potentially speeding up workflows for users.

RANK_REASON This is a LoRA, which is a technique for fine-tuning existing models, not a new frontier model release or significant research paper.

Read on r/StableDiffusion →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New 8-step LoRA for MOSS-Image-Base reduces inference steps

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This is a LoRA, which is a technique for fine-tuning existing models, not a new frontier model release or significant research paper.
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COVERAGE [1]

  1. r/StableDiffusion TIER_2 English(EN) · /u/mikemend ·

    A new 8-steps DMD2 Lora for MOSS

    <!-- SC_OFF --><div class="md"><p>An 8-step LoRA (rank 128) for <a href="https://huggingface.co/OpenMOSS-Team/MOSS-Image-Base">MOSS-Image-Base</a>, distilled by distribution matching: 8 forwards at CFG 1, without a negative prompt, in place of the base model's 50 guided steps. It…

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