Comments (2)
Hi,
Thanks for the question.
You need to first train a classifier using this command:
python train_run.py --experiment e2e-back --app "--init_emb diff_e2e-tgt_pad_rand16_transformer_lr0.0001_0.0_2000_sqrt_Lsimple_h128_s2_d0.1_sd102_xstart_e2e --n_embd 16 --learned_emb yes " --notes "full_multi_sqrt_16" --epoch 6 --bsz 10
[you might want to modify the --init_emb command, which is the path to your diffusion model, Or you can download it here: https://drive.google.com/drive/folders/110CA22rwu_3EcllPYGhql0TnYeOBY77d and set init_emb to this path. ]
Then to execute the controllable generation:
python scripts/infill.py --model_path /u/scr/nlp/xlisali/predictability/diffusion_models_ablations/diff_e2e-tgt_pad_rand16_transformer_lr0.0001_0.0_2000_sqrt_Lsimple_h128_s2_d0.1_sd102_xstart_e2e/ema_0.9999_200000.pt --eval_task_ 'control_attribute' --use_ddim True --notes "tree_full_adagrad" --eta 1. --verbose pipe --lr_coef 0.0005
Again, you might want to change the model_path to the diffusion path.
Hope this helps!
from diffusion-lm.
Hi,
Thanks for the question. You need to first train a classifier using this command:
python train_run.py --experiment e2e-back --app "--init_emb diff_e2e-tgt_pad_rand16_transformer_lr0.0001_0.0_2000_sqrt_Lsimple_h128_s2_d0.1_sd102_xstart_e2e --n_embd 16 --learned_emb yes " --notes "full_multi_sqrt_16" --epoch 6 --bsz 10
[you might want to modify the --init_emb command, which is the path to your diffusion model, Or you can download it here: https://drive.google.com/drive/folders/110CA22rwu_3EcllPYGhql0TnYeOBY77d and set init_emb to this path. ]
Then to execute the controllable generation:
python scripts/infill.py --model_path /u/scr/nlp/xlisali/predictability/diffusion_models_ablations/diff_e2e-tgt_pad_rand16_transformer_lr0.0001_0.0_2000_sqrt_Lsimple_h128_s2_d0.1_sd102_xstart_e2e/ema_0.9999_200000.pt --eval_task_ 'control_attribute' --use_ddim True --notes "tree_full_adagrad" --eta 1. --verbose pipe --lr_coef 0.0005
Again, you might want to change the model_path to the diffusion path.
Hope this helps!
Helps a lot !
But I still need some help.馃槶
I don't know how to train my own diffusion model for this task, is it the same command as
"python scripts/run_train.py --diff_steps 2000 --model_arch transformer --lr 0.0001 --lr_anneal_steps 200000 --seed 102 --noise_schedule sqrt --in_channel 16 --modality e2e-tgt --submit no --padding_mode block --app "--predict_xstart True --training_mode e2e --vocab_size 821 --e2e_train ../datasets/e2e_data " --notes xstart_e2e"
in README?
Specifically, do some configs need to be changed, such as training_mode
or the modality
, if so, what should they be changed?
Thanks again!
from diffusion-lm.
Related Issues (20)
- I wander where to find the model in the predictability HOT 1
- Training on A100
- Separate weights for word embedding and lm-head?
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- Error when running training script on Google Colab HOT 2
- Fail to load GPT2 pretrained model for attribute controled generation
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- About the tT_loss HOT 1
- The difference between this code and the paper "IDDPM" in the run_loop function in train_util.py.
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