Bubbliiiingandhkunzhe
9f34f7c8c5
* Update Flow * Update Flow * Update Flow * add image recaptioning * Fix bug in t2v * update train_reward_lora.py * update reward training * Update V5.1 and mix multi text_encoders to one pipeline * Update V5.1 training Code * Update ComfyUI * Update Comment * Delete files * update reward training * Update Readme * fix extract frames in compute_semantic_consistency * Update Readme && Remove to in prediction * Update Demo * Update Readme * Update ui * support vae gradient checkpointing in reward training * Update Training Readme --------- Co-authored-by: hkunzhe <huangkunzhe.hkz@alibaba-inc.com>
76 lines
2.9 KiB
Bash
76 lines
2.9 KiB
Bash
export MODEL_NAME="models/Diffusion_Transformer/EasyAnimateV5-12b-zh-InP"
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export TRAIN_PROMPT_PATH="MovieGenVideoBench_train.txt"
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# Performing validation simultaneously with training will increase time and GPU memory usage.
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export VALIDATION_PROMPT_PATH="MovieGenVideoBench_val.txt"
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export NCCL_IB_DISABLE=1
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export NCCL_P2P_DISABLE=1
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NCCL_DEBUG=INFO
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# When train model with multi machines, use "--config_file accelerate.yaml" instead of "--mixed_precision='bf16'".
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accelerate launch --num_processes=8 --mixed_precision="bf16" --use_deepspeed --deepspeed_config_file config/zero_stage2_config.json scripts/train_reward_lora.py \
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--pretrained_model_name_or_path=$MODEL_NAME \
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--config_path="config/easyanimate_video_v5_magvit_multi_text_encoder.yaml" \
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--train_batch_size=1 \
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--gradient_accumulation_steps=1 \
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--max_train_steps=10000 \
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--checkpointing_steps=100 \
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--learning_rate=1e-05 \
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--seed=42 \
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--output_dir="output_dir" \
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--gradient_checkpointing \
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--mixed_precision="bf16" \
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--adam_weight_decay=3e-2 \
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--adam_epsilon=1e-10 \
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--max_grad_norm=0.3 \
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--low_vram \
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--use_deepspeed \
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--prompt_path=$TRAIN_PROMPT_PATH \
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--train_sample_height=256 \
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--train_sample_width=256 \
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--video_length=49 \
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--validation_prompt_path=$VALIDATION_PROMPT_PATH \
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--validation_steps=100 \
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--validation_batch_size=8 \
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--num_decoded_latents=1 \
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--reward_fn="HPSReward" \
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--reward_fn_kwargs='{"version": "v2.1"}' \
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--backprop
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# For V5.1
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# export MODEL_NAME="models/Diffusion_Transformer/EasyAnimateV5.1-12b-zh-InP"
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# export TRAIN_PROMPT_PATH="MovieGenVideoBench_train.txt"
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# # Performing validation simultaneously with training will increase time and GPU memory usage.
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# export VALIDATION_PROMPT_PATH="MovieGenVideoBench_val.txt"
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# export NCCL_IB_DISABLE=1
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# export NCCL_P2P_DISABLE=1
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# NCCL_DEBUG=INFO
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# # When train model with multi machines, use "--config_file accelerate.yaml" instead of "--mixed_precision='bf16'".
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# accelerate launch --num_processes=8 --mixed_precision="bf16" --use_deepspeed --deepspeed_config_file config/zero_stage2_config.json scripts/train_reward_lora.py \
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# --pretrained_model_name_or_path=$MODEL_NAME \
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# --config_path="config/easyanimate_video_v5.1_magvit_qwen.yaml" \
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# --train_batch_size=1 \
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# --gradient_accumulation_steps=1 \
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# --max_train_steps=10000 \
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# --checkpointing_steps=100 \
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# --learning_rate=1e-05 \
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# --seed=42 \
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# --output_dir="output_dir" \
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# --gradient_checkpointing \
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# --mixed_precision="bf16" \
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# --adam_weight_decay=3e-2 \
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# --adam_epsilon=1e-10 \
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# --max_grad_norm=0.3 \
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# --low_vram \
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# --use_deepspeed \
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# --prompt_path=$TRAIN_PROMPT_PATH \
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# --train_sample_height=256 \
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# --train_sample_width=256 \
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# --video_length=49 \
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# --num_decoded_latents=1 \
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# --reward_fn="HPSReward" \
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# --reward_fn_kwargs='{"version": "v2.1"}' \
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# --backprop_strategy "tail" \
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# --backprop_num_steps 10 |