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Community: config-driven Lens-Base LoRA trainer (flow-match, Comfy export) — working on 16GB

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还没有人认领这个 Issue。

评估

难度
4/5
预计耗时
3-5 天
新手友好度
30/100
Issue 类型
功能
描述清晰度
需要澄清
活跃度
冷清
技术栈
python

调研方向

Start with the linked LoboForge-LensTrainer repository, especially train.py, configs/train_lora_lens_base_24gb.yaml, and the Sebastian/Jessica v2 writeup. The issue demonstrates an external config-driven LoRA workflow but does not identify an in-repository change or acceptance criteria, so a maintainer must first decide whether any integration, documentation, or follow-up is wanted.

由索引模型根据 Issue 内容生成。

描述

Hi Lens team,

We've built and validated a standalone LoRA trainer for microsoft/Lens-Base and wanted to share it in case it's useful to the community or future official tooling.

Trainer repo: https://github.com/LoboForge/LoboForge-LensTrainer
Hugging Face Space: https://huggingface.co/spaces/LoboForge/LoboForge-LensTrainer
HF collection: https://huggingface.co/collections/LoboForge/lens-training-loboforge

What works today

  • Config-driven training: python train.py configs/train_lora_lens_base_24gb.yaml
  • Flow-match loss aligned with LensPipeline (latents [B, H×W, 128], GPT-OSS multi-layer text, timestep/1000)
  • PEFT LoRA on LensTransformer2DModel; TE/VAE frozen
  • ComfyUI-compatible export (diffusion_model.* key remap)
  • 24GB preset: CPU offload, TE + latent cache, disable_mxfp4, grad checkpointing, AdamW 8-bit
  • Checkpoint resume, mid-training samples, loss.json

Example result (community LoRA, not Microsoft-endorsed)

Environment

  • Lens installed from vendor/Lens (git clone; upstream has no pyproject.toml)
  • Tested on consumer/datacenter GPUs with Lens-Base local or HF hub

We're not asking for an immediate merge — mainly flagging that Lens-Base LoRA training is practical with the public inference package, and happy to answer questions or share configs/logs if helpful.

Thanks for open-sourcing Lens!

主要语言
Python
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2
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0
PR 合并指标
30 天内没有已合并 PR

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这个项目没有提供开发容器、Dockerfile 或贡献指南,环境需要你自己搭建:先看它的 README,通用步骤见我们的新手贡献指南。

从这里开始

  1. 先读完整个 Issue,再读项目的贡献指南。
  2. 在 Issue 下留言说明你要接手 —— 这能避免两个人做同样的事。
  3. Fork 仓库,在一个分支上完成修改。
  4. 提交 Pull Request,并在描述里引用这个 Issue 编号。

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