${pip_ext} install image-reward pytorch_lightning

${pip_ext} install -U timm diffusers

${pip_ext} install openai==1.34.0

${pip_ext} install httpx==0.20.0

# step 1, infer images

${python_ext} evaluation/image_reward/infer4eval.py \

--cfg ${cfg} \

--tau ${tau} \

--pn ${pn} \

--model_path ${infinity_model_path} \

--vae_type ${vae_type} \

--vae_path ${vae_path} \

--add_lvl_embeding_only_first_block ${add_lvl_embeding_only_first_block} \

--use_bit_label ${use_bit_label} \

--model_type ${model_type} \

--rope2d_each_sa_layer ${rope2d_each_sa_layer} \

--rope2d_normalized_by_hw ${rope2d_normalized_by_hw} \

--use_scale_schedule_embedding ${use_scale_schedule_embedding} \

--cfg ${cfg} \

--tau ${tau} \

--checkpoint_type ${checkpoint_type} \

--text_encoder_ckpt ${text_encoder_ckpt} \

--text_channels ${text_channels} \

--apply_spatial_patchify ${apply_spatial_patchify} \

--cfg_insertion_layer ${cfg_insertion_layer} \

--outdir ${out_dir}

# step 2, compute image reward

${pip_ext} install diffusers==0.16.0

${pip_ext} install git+https://github.com/openai/CLIP.git ftfy

${python_ext} evaluation/image_reward/cal_imagereward.py \

--meta_file ${out_dir}/metadata.jsonl

${pip_ext} install hpsv2

${pip_ext}install -U diffusers

sudo apt install python3-tk

wget https://dl.fbaipublicfiles.com/mmf/clip/bpe_simple_vocab_16e6.txt.gz

mv bpe_simple_vocab_16e6.txt.gz /home/tiger/.local/lib/python3.9/site-packages/hpsv2/src/open_clip

mkdir -p ${out_dir}

${python_ext} evaluation/hpsv2/eval_hpsv2.py \

--cfg ${cfg} \

--tau ${tau} \

--pn ${pn} \

--model_path ${infinity_model_path} \

--vae_type ${vae_type} \

--vae_path ${vae_path} \

--add_lvl_embeding_only_first_block ${add_lvl_embeding_only_first_block} \

--use_bit_label ${use_bit_label} \

--model_type ${model_type} \

--rope2d_each_sa_layer ${rope2d_each_sa_layer} \

--rope2d_normalized_by_hw ${rope2d_normalized_by_hw} \

--use_scale_schedule_embedding ${use_scale_schedule_embedding} \

--cfg ${cfg} \

--tau ${tau} \

--checkpoint_type ${checkpoint_type} \

--text_encoder_ckpt ${text_encoder_ckpt} \

--text_channels ${text_channels} \

--apply_spatial_patchify ${apply_spatial_patchify} \

--cfg_insertion_layer ${cfg_insertion_layer} \

--outdir ${out_dir}/images | tee ${out_dir}/log.txt

${pip_ext} install -U openmim

mim install mmengine mmcv-full==1.7.2

${pip_ext} install mmdet==2.28.2 pytorch_lightning clip_benchmark open-clip-torch==2.20.0

${pip_ext} install -U diffusers

sudo apt install libgl1

${pip_ext} install openai

${pip_ext} install httpx==0.20.0

# run inference

${python_ext} evaluation/gen_eval/infer4eval.py \

--cfg ${cfg} \

--tau ${tau} \

--pn ${pn} \

--model_path ${infinity_model_path} \

--vae_type ${vae_type} \

--vae_path ${vae_path} \

--add_lvl_embeding_only_first_block ${add_lvl_embeding_only_first_block} \

--use_bit_label ${use_bit_label} \

--model_type ${model_type} \

--rope2d_each_sa_layer ${rope2d_each_sa_layer} \

--rope2d_normalized_by_hw ${rope2d_normalized_by_hw} \

--use_scale_schedule_embedding ${use_scale_schedule_embedding} \

--cfg ${cfg} \

--tau ${tau} \

--checkpoint_type ${checkpoint_type} \

--text_encoder_ckpt ${text_encoder_ckpt} \

--text_channels ${text_channels} \

--apply_spatial_patchify ${apply_spatial_patchify} \

--cfg_insertion_layer ${cfg_insertion_layer} \

--outdir ${out_dir}/images \

--rewrite_prompt ${rewrite_prompt}

# detect objects

${python_ext} evaluation/gen_eval/evaluate_images.py ${out_dir}/images \

--outfile ${out_dir}/results/det.jsonl \

--model-config evaluation/gen_eval/mask2former/mask2former_swin-s-p4-w7-224_lsj_8x2_50e_coco.py \

--model-path weights/mask2former

# accumulate results

${python_ext} evaluation/gen_eval/summary_scores.py ${out_dir}/results/det.jsonl > ${out_dir}/results/res.txt

cat ${out_dir}/results/res.txt

${pip_ext} install pytorch_fid

# step 1, infer images

${python_ext} tools/comprehensive_infer.py \

--cfg ${cfg} \

--tau ${tau} \

--pn ${pn} \

--model_path ${infinity_model_path} \

--vae_type ${vae_type} \

--vae_path ${vae_path} \

--add_lvl_embeding_only_first_block ${add_lvl_embeding_only_first_block} \

--use_bit_label ${use_bit_label} \

--model_type ${model_type} \

--rope2d_each_sa_layer ${rope2d_each_sa_layer} \

--rope2d_normalized_by_hw ${rope2d_normalized_by_hw} \

--use_scale_schedule_embedding ${use_scale_schedule_embedding} \

--cfg ${cfg} \

--tau ${tau} \

--checkpoint_type ${checkpoint_type} \

--text_encoder_ckpt ${text_encoder_ckpt} \

--text_channels ${text_channels} \

--apply_spatial_patchify ${apply_spatial_patchify} \

--cfg_insertion_layer ${cfg_insertion_layer} \

--coco30k_prompts 0 \

--save4fid_eval 1 \

--jsonl_filepath ${jsonl_filepath} \

--long_caption_fid ${long_caption_fid} \

--out_dir ${out_dir} \

# step 2, compute fid

${python_ext} tools/fid_score.py \

${out_dir}/pred \

${out_dir}/gt | tee ${out_dir}/log.txt

${python_ext} evaluation/validation_loss/validation_loss.py \

--cfg ${cfg} \

--tau ${tau} \

--pn ${pn} \

--model_path ${infinity_model_path} \

--vae_type ${vae_type} \

--vae_path ${vae_path} \

--add_lvl_embeding_only_first_block ${add_lvl_embeding_only_first_block} \

--use_bit_label ${use_bit_label} \

--model_type ${model_type} \

--rope2d_each_sa_layer ${rope2d_each_sa_layer} \

--rope2d_normalized_by_hw ${rope2d_normalized_by_hw} \

--use_scale_schedule_embedding ${use_scale_schedule_embedding} \

--cfg ${cfg} \

--tau ${tau} \

--checkpoint_type ${checkpoint_type} \

--text_encoder_ckpt ${text_encoder_ckpt} \

--text_channels ${text_channels} \

--apply_spatial_patchify ${apply_spatial_patchify} \

--cfg_insertion_layer ${cfg_insertion_layer} \

--save_dir ${out_dir} \

--reweight_loss_by_scale ${reweight_loss_by_scale} \

--meta_folder ${jsonl_folder} \

--noise_apply_strength ${noise_apply_strength} \

--bf16 0 \

--log_freq 10