Furkan Gözükara
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How To Start Multiple Automatic1111 Web UI And Kohya Training On A Single Pod
🕑 Added 2023-09-12 10:30:39 +0000 UTC
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Download the attached webui-user.sh and relauncher.py into the workspace/stable-diffusion-webui
It will ask you to overwrite. Overwrite them.
Edit relauncher . py file and change the GPU device index
- CUDA_VISIBLE_DEVICES=0 : The web UI will start on first GPU
- CUDA_VISIBLE_DEVICES=1 : The web UI will start on second GPU
- CUDA_VISIBLE_DEVICES=2 : The web UI will start on third GPU and so on
To start web ui call
- python relauncher.py
- This will start web UI instance and it will give you a public Gradio link to use them
Or download files as files as zip.zip
To start multiple Kohya trainings on a single Pod:
With multiple GPUs you need to first get your training command. You can use Kohya GUI print training command feature
Then you need to add below to the beginning of the command
First activate venv
- cd /workspace/kohya_ss
- source venv/bin/activate
- Then below
Example (it starts training on third GPU on the machine)
CUDA_VISIBLE_DEVICES=3 accelerate launch --num_cpu_threads_per_process=4 "./sdxl_train.py" --pretrained_model_name_or_path="/workspace/stable-diffusion-webui/models/Stable-diffusion/sd_xl_base_1.0.safetensors" --train_data_dir="/workspace/stable-diffusion-webui/models/Stable-diffusion/img" --reg_data_dir="/workspace/stable-diffusion-webui/models/Stable-diffusion/reg" --resolution="1024,1024" --output_dir="/workspace/stable-diffusion-webui/models/Stable-diffusion/model" --logging_dir="/workspace/stable-diffusion-webui/models/Stable-diffusion/log" --save_model_as=safetensors --output_name="1e5_ada_40_repeat_wd001" --lr_scheduler_num_cycles="8" --max_data_loader_n_workers="0" --learning_rate="1e-05" --lr_scheduler="constant" --train_batch_size="1" --max_train_steps="5200" --save_every_n_epochs="1" --mixed_precision="bf16" --save_precision="bf16" --cache_latents --cache_latents_to_disk --optimizer_type="adafactor" --max_data_loader_n_workers="0" --bucket_reso_steps=64 --xformers --bucket_no_upscale --noise_offset=0.0 --full_bf16 --optimizer_args scale_parameter=False relative_step=False warmup_init=False weight_decay=0.01
Comments
Furkan Gözükara
You are welcome. Thank you for supporting me
baran mengül
Thank you
Furkan Gözükara
it changes according to the optimizer. adafactor is 1.05 it / s and lion is 1.55 it / s on A5000. or AdamW8Bit is also 1.55 it / s on A5000 GPU - same price as RTX 3090
baran mengül
How much does ıt/s go up ? thanks
Furkan Gözükara
thank you so much
Rafał Ryniak
Wow, 👏🏻you are great!
