lllyasviel / lllyasviel/stable-diffusion-webui-forge
Using Lora Causes OOM And Fills Ram
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- Dominant language
- Python
- Stars
- 13k
- Forks
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Description
### **Problem**
**(Im talking about Ram Not Video Card Memory/Vram)**
When using Any Lora in Normal SD Caused OOM Or my Ram getting filled up not unloading then i tried forge and its happening here as well.
Normal generation without Lora is fine and it dose not use 90% of my memory and it can generate and unload ram fine.
But as soon as i put Any Kinda of Lora on any Model it will fill 85% to 90% of my ram and crash on generating.
### **What did i try?**
I tried --medvram and --lowvram didn't solve it.
I tried Deleting Venv Folder and reinstalling didn't solve it.
i tried Reinstalling Whole forge and everything didn't solve it.
i tried reinstalling windows didn't solve it.
### **System Spec**
Ram: 16Gb ddr4
Gpu: Rtx 3060 12gb
### **webui-user.bat Settings**
@echo off
set PYTHON=
set GIT=
set VENV_DIR=
set COMMANDLINE_ARGS= --no-download-sd-model --opt-split-attention --no-gradio-queue --skip-torch-cuda-test --opt-channelslast --autolaunch --ckpt-dir "D:\Ai\stable-webui\webui\models\Stable-diffusion" --lora-dir "D:\Ai\stable-webui\webui\models\Lora"
set CUDA_LAUNCH_BLOCKING=1
set SAFETENSORS_FAST_GPU=1
call webui.bat
### **Questions**
1.How do i fix it?
2.Are there any solutions for it like going back to a more stable version.
3.what version of forge or stable Dif you recommend me using?
Contributor guide
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First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
Start with the reported webui-user.bat settings and reproduce generation with and without a LoRA on the stated 16 GB RAM and RTX 3060 system. Compare memory behavior and determine whether the issue can be isolated to LoRA loading; done means the RAM exhaustion is explained and a verified fix or clear reproducible finding is documented.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python
- Domain
- machine-learning
- Issue type
- Bug
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 25/100