train_script

train_scripts.sh

@@ -0,0 +1,45 @@
+# nvidia-smi | grep 'python' | awk '{ print $5 }' | xargs -n1 kill -9
+
+timestamp=$(date +%y%m%d_%H%M%S)
+DATASET="nuscenes" #"kitti/vkitti/bdd100k/nuscenes/..."
+DATASET_PATH="/network/scratch/a/anthony.gosselin"
+NAME="${DATASET}_svd_7hz_3"
+OUT_DIR="/network/scratch/a/anthony.gosselin/Results/ctrlv/${NAME}"
+mkdir -p $OUT_DIR
+
+PROJECT_NAME='ctrl-v'
+
+SCRIPT_PATH=$0
+SAVE_SCRIPT_PATH="${OUT_DIR}/train_scripts.sh"
+cp $SCRIPT_PATH $SAVE_SCRIPT_PATH
+echo "Saved script to ${SAVE_SCRIPT_PATH}"
+
+CUDA_LAUNCH_BLOCKING=1 accelerate launch tools/train_video_diffusion.py \
+    --run_name $NAME \
+    --data_root $DATASET_PATH \
+    --project_name $PROJECT_NAME \
+    --pretrained_model_name_or_path stabilityai/stable-video-diffusion-img2vid-xt \
+    --output_dir $OUT_DIR \
+    --variant fp16 \
+    --dataset_name $DATASET \
+    --train_batch_size 1 \
+    --learning_rate 1e-5 \
+    --checkpoints_total_limit 1 \
+    --checkpointing_steps 300 \
+    --gradient_accumulation_steps 5 \
+    --validation_steps 300 \
+    --enable_gradient_checkpointing \
+    --lr_scheduler constant \
+    --report_to wandb \
+    --seed 1234 \
+    --mixed_precision no \
+    --clip_length 25 \
+    --min_guidance_scale 1.0 \
+    --max_guidance_scale 3.0 \
+    --noise_aug_strength 0.01 \
+    --bbox_dropout_prob 0.1 \
+    --num_demo_samples 15 \
+    --backprop_temporal_blocks_start_iter -1 \
+    --num_train_epochs 20 \
+    --resume_from_checkpoint latest \
+    --wandb_entity chris-pal