laion-ai / ongo

Generate a painting using text.

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Ongo

Ongo Gablogian, the art collector. Charmed, I’m sure.

This repo is modified from glid-3-xl.

Checkpoints are finetuned from glid-3-xl inpaint.pt and can be downloaded manually here: ongo.pt.

Aesthetic CLIP embeds are provided by aesthetic-predictor

Install

virtual environment:

python3 -m venv .venv
source venv/bin/activate
(venv) $

pytorch

(venv) $ pip install torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cu113

latent-diffusion/taming-transformers


(venv) $ git clone https://github.com/CompVis/latent-diffusion.git
(venv) $ git clone https://github.com/CompVis/taming-transformers
(venv) $ pip install -e ./taming-transformers
(venv) $ pip install omegaconf>=2.0.0 pytorch-lightning>=1.0.8 torch-fidelity einops

latent-diffusion-finetune

(venv) $ git clone https://github.com/laion-ai/ldm-finetune.git
(venv) $ cd latent-diffusion-finetune
(venv) $ pip install -e .

Download model files

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

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

# inpaint
(venv) $ wget https://dall-3.com/models/glid-3-xl/inpaint.pt

# erlich
(venv) $ wget https://huggingface.co/laion/erlich/raw/main/model/ema_0.9999_120000.pt

# ongo - tbd
(venv) $ wget TODO

Generating images

note: best results at 256x256 image size

# 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

Editing images

aka human guided diffusion. You can use inpainting to generate more complex prompts by progressively editing the image

note: you can use > 256px but the model only sees 256x256 at a time, so ensure the inpaint area is smaller than that

note: inpaint training wip


# 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"

Training/Fine tuning

Train with same flags as guided diffusion. Data directory should contain image and text files with the same name (image1.png image1.txt)

# 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

Train for inpainting

# 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