Hyperparameters on training SD1.5

[AndysonYs]

Hi. Thanks for your insightful work. Could you share us the hyperparams of training SD1.5 on Laion dataset?

• Which Laion dataset did you use? laion2b or aesthetic or something else?
• How many GPU did you use? what is the batch size per GPU?
• I find there is a script in your repo and it seems to train SD1.5. But i cannot find the exactly same pretrained ckpt. And I do not know how many iteration do you train it.

perflow_accelerate_sd.py
--data_root "???"
--resolution 512 --dataloader_num_workers 8 --train_batch_size 32 --gradient_accumulation_steps 1
--pretrained_model_name_or_path "../assets/public_models/DreamBooth/sd15_eps/DreamShaper_8_pruned"
--unet_model_path ""
--pred_type "diff_eps" --loss_type "noise_matching"
--windows 4 --solving_steps 8 --support_cfg --cfg_sync
--learning_rate 8e-5 --lr_scheduler "constant" --lr_warmup_steps 500 --use_ema
--mixed_precision "fp16"
--output_dir "../exps/sd15ds_perflow_4ddim8_diffeps_cfgsync"
--validation_steps 100 --inference_steps "8-4" --inference_cfg "7.5-4.5" --save_ckpt_state --checkpointing_steps 1000

[AndysonYs]

I've tried this and i got nearly the same result as #17 . It only produce noise after 4k iters. Could you help me on it?

I used the 1M subset of laion/laion2B-en-aesthetic and stable-diffusion-v1-5/stable-diffusion-v1-5. I ran it on 32 GPUs with a total bs of 1024.
Here is my script.

accelerate launch
--main_process_port 16323
--num_processes 32
--num_cpu_threads_per_process 6
./scripts/perflow_accelerate_sd.py
--data_root "???"
--resolution 512 --dataloader_num_workers 8 --train_batch_size 32 --gradient_accumulation_steps 1
--pretrained_model_name_or_path "stable-diffusion-v1-5/stable-diffusion-v1-5"
--unet_model_path ""
--pred_type "diff_eps" --loss_type "noise_matching"
--windows 4 --solving_steps 8 --support_cfg --cfg_sync
--learning_rate 1e-5 --lr_scheduler "constant" --lr_warmup_steps 500 --use_ema
--mixed_precision "fp16"
--output_dir "../exps_ys/sd15_laion_train"
--validation_steps 1000 --inference_steps "4-8" --inference_cfg "7.5" --save_ckpt_state --checkpointing_steps 2000
--max_train_steps 100000