# then
git clone https://github.com/Jack000/glid-3-xl
cd glid-3-xl
pip install -e .

# text encoder (required)
wget https://dall-3.com/models/glid-3-xl/bert.pt

# ldm first stage (required)
wget https://dall-3.com/models/glid-3-xl/kl-f8.pt

# there are several diffusion models to choose from:

# original diffusion model from CompVis
wget https://dall-3.com/models/glid-3-xl/diffusion.pt

# new model fine tuned on a cleaner dataset (will not generate watermarks, split images or blurry images)
wget https://dall-3.com/models/glid-3-xl/finetune.pt

# inpaint
wget https://dall-3.com/models/glid-3-xl/inpaint.pt

# fast PLMS sampling
python sample.py --model_path finetune.pt --batch_size 6 --num_batches 6 --text "a cyberpunk girl with a scifi neuralink device on her head"

# classifier free guidance + CLIP guidance (better adherence to prompt, much slower)
python sample.py --clip_guidance --model_path finetune.pt --batch_size 1 --num_batches 12 --text "a cyberpunk girl with a scifi neuralink device on her head | trending on artstation"

# sample with an init image
python sample.py --init_image picture.jpg --skip_timesteps 10 --model_path finetune.pt --batch_size 6 --num_batches 6 --text "a cyberpunk girl with a scifi neuralink device on her head"

# generated images saved to ./output/
# generated image embeddings saved to ./output_npy/ as npy files

# install PyQt5 if you want to use a gui, otherwise supply a mask file
pip install PyQt5

# this will pop up a window, use your mouse to paint
# use the generated npy files instead of png for best quality
python sample.py --model_path inpaint.pt --edit output_npy/00000.npy --batch_size 6 --num_batches 6 --text "your prompt"

# after painting, the mask is saved for re-use
python sample.py --mask mask.png --model_path inpaint.pt --edit output_npy/00000.npy --batch_size 6 --num_batches 6 --text "your prompt"

# additional arguments for uncropping
python sample.py --edit_x 64 --edit_y 64 --edit_width 128 --edit_height 128 --model_path inpaint.pt --edit output_npy/00000.npy --batch_size 6 --num_batches 6 --text "your prompt"

# autoedit uses the inpaint model to give the ldm an image prompting function (that works differently from --init_image)
# it continuously edits random parts of the image to maximize clip score for the text prompt
python autoedit.py --edit image.png --model_path inpaint.pt --batch_size 6 --text "your prompt"

# not possible to train on 24gb vram currently!
MODEL_FLAGS="--ema_rate 0.9999 --attention_resolutions 32,16,8 --class_cond False --diffusion_steps 1000 --image_size 32 --learn_sigma False --noise_schedule linear --num_channels 320 --num_heads 8 --num_res_blocks 2 --resblock_updown False --use_fp16 True --use_scale_shift_norm False"
TRAIN_FLAGS="--lr 1e-5 --batch_size 64 --microbatch 1 --log_interval 1 --save_interval 5000 --kl_model kl-f8.pt --bert_model bert.pt --resume_checkpoint diffusion.pt"
export OPENAI_LOGDIR=./logs/
export TOKENIZERS_PARALLELISM=false
python scripts/image_train_latent.py --data_dir /path/to/data $MODEL_FLAGS $TRAIN_FLAGS

# batch size > 1 required
MODEL_FLAGS="--dropout 0.1 --ema_rate 0.9999 --attention_resolutions 32,16,8 --class_cond False --diffusion_steps 1000 --image_size 32 --learn_sigma False --noise_schedule linear --num_channels 320 --num_heads 8 --num_res_blocks 2 --resblock_updown False --use_fp16 True --use_scale_shift_norm False"
TRAIN_FLAGS="--lr --batch_size 64 --microbatch 1 --log_interval 1 --save_interval 5000 --kl_model kl-f8.pt --bert_model bert.pt --resume_checkpoint diffusion.pt"
export OPENAI_LOGDIR=./logs/
export TOKENIZERS_PARALLELISM=false
python scripts/image_train_inpaint.py --data_dir /path/to/data $MODEL_FLAGS $TRAIN_FLAGS