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# This script is used for Pyramid-Flow Image and Video Generation Training (without using Temporal Pyramid and autoregressive training) | |
# Since the design of spatial pyramid and temporal pyramid are decoupled, we can only use the spatial pyramid flow | |
# to train with full-sequence diffusion, which is also more effective than the normal flow matching training strategy | |
GPUS=8 # The gpu number | |
TASK=t2i # t2i or t2v | |
SHARD_STRATEGY=zero2 # zero2 or zero3 | |
MODEL_NAME=pyramid_flux # The model name, `pyramid_flux` or `pyramid_mmdit` | |
MODEL_PATH=/PATH/pyramid-flow-miniflux # The downloaded ckpt dir. IMPORTANT: It should match with model_name, flux or mmdit (sd3) | |
VARIANT=diffusion_transformer_image # The DiT Variant, diffusion_transformer_image or diffusion_transformer_384p | |
OUTPUT_DIR=/PATH/output_dir # The checkpoint saving dir | |
NUM_FRAMES=8 # e.g., 8 for 2s, 16 for 5s, 32 for 10s | |
BATCH_SIZE=4 # It should satisfy batch_size % 4 == 0 | |
RESOLUTION="768p" # 384p or 768p | |
ANNO_FILE=annotation/image_text.jsonl # The annotation file path | |
torchrun --nproc_per_node $GPUS \ | |
train/train_pyramid_flow.py \ | |
--num_workers 8 \ | |
--task $TASK \ | |
--use_fsdp \ | |
--fsdp_shard_strategy $SHARD_STRATEGY \ | |
--use_flash_attn \ | |
--load_text_encoder \ | |
--load_vae \ | |
--model_name $MODEL_NAME \ | |
--model_path $MODEL_PATH \ | |
--model_dtype bf16 \ | |
--model_variant $VARIANT \ | |
--schedule_shift 1.0 \ | |
--gradient_accumulation_steps 1 \ | |
--output_dir $OUTPUT_DIR \ | |
--batch_size $BATCH_SIZE \ | |
--max_frames $NUM_FRAMES \ | |
--resolution $RESOLUTION \ | |
--anno_file $ANNO_FILE \ | |
--frame_per_unit 1 \ | |
--lr_scheduler constant_with_warmup \ | |
--opt adamw \ | |
--opt_beta1 0.9 \ | |
--opt_beta2 0.95 \ | |
--seed 42 \ | |
--weight_decay 1e-4 \ | |
--clip_grad 1.0 \ | |
--lr 1e-4 \ | |
--warmup_steps 1000 \ | |
--epochs 20 \ | |
--iters_per_epoch 2000 \ | |
--report_to tensorboard \ | |
--print_freq 40 \ | |
--save_ckpt_freq 1 |