concept-ablation / trainer.py
Nupur Kumari
concept ablation
8173ae1
raw
history blame
5.13 kB
import gradio as gr
import PIL.Image
import shlex
import shutil
import subprocess
from pathlib import Path
import os
import torch
from tqdm import tqdm
def pad_image(image: PIL.Image.Image) -> PIL.Image.Image:
w, h = image.size
if w == h:
return image
elif w > h:
new_image = PIL.Image.new(image.mode, (w, w), (0, 0, 0))
new_image.paste(image, (0, (w - h) // 2))
return new_image
else:
new_image = PIL.Image.new(image.mode, (h, h), (0, 0, 0))
new_image.paste(image, ((h - w) // 2, 0))
return new_image
def train_submit(
prompt, anchor_prompt, concept_type, reg_lambda, iterations, lr, openai_key, save_path, mem_impath=None
):
if not torch.cuda.is_available():
raise gr.Error('CUDA is not available.')
torch.cuda.empty_cache()
original_prompt = prompt
parameter_group = "cross-attn"
train_batch_size = 4
if concept_type == 'style':
class_data_dir = f'./data/samples_painting/'
anchor_prompt = f'./assets/painting.txt'
openai_key = ''
elif concept_type == 'object':
os.makedirs('temp', exist_ok=True)
class_data_dir = f'./temp/{anchor_prompt}'
name = save_path.split('/')[-1]
prompt = f'{anchor_prompt}+{prompt}'
assert openai_key is not None
if len(openai_key.split('\n')) > 1:
openai_key = openai_key.split('\n')
with open(f'./temp/{name}.txt', 'w') as f:
for prompt_ in openai_key:
f.write(prompt_.strip()+'\n')
openai_key = ''
anchor_prompt = f'./temp/{name}.txt'
elif concept_type == 'memorization':
os.system("wget https://dl.fbaipublicfiles.com/sscd-copy-detection/sscd_imagenet_mixup.torchscript.pt -P assets/")
os.makedirs('temp', exist_ok=True)
prompt = f'*+{prompt}'
name = save_path.split('/')[-1]
train_batch_size = 1
lr = 5e-7
parameter_group = "full-weight"
assert openai_key is not None
assert mem_impath is not None
if len(openai_key.split('\n')) > 1:
openai_key = openai_key.split('\n')
with open(f'./temp/{name}.txt', 'w') as f:
for prompt_ in openai_key:
f.write(prompt_.strip()+'\n')
openai_key = ''
anchor_prompt = f'./temp/{name}.txt'
else:
anchor_prompt = prompt
print(mem_impath)
image = PIL.Image.open(mem_impath[0][0].name)
image = pad_image(image)
image = image.convert('RGB')
mem_impath = f"./temp/{original_prompt.lower().replace(' ', '')}.jpg"
image.save(mem_impath, format='JPEG', quality=100)
class_data_dir = f"./temp/{original_prompt.lower().replace(' ', '')}"
command = f'''
accelerate launch concept-ablation-diffusers/train.py \
--pretrained_model_name_or_path="CompVis/stable-diffusion-v1-4" \
--output_dir={save_path} \
--class_data_dir={class_data_dir} \
--class_prompt="{anchor_prompt}" \
--caption_target "{prompt}" \
--concept_type {concept_type} \
--resolution=512 \
--train_batch_size={train_batch_size} \
--learning_rate={lr} \
--max_train_steps={iterations} \
--scale_lr --hflip \
--parameter_group {parameter_group} \
--openai_key "{openai_key}" \
--enable_xformers_memory_efficient_attention --num_class_images 500
'''
if concept_type == 'style':
command += f' --noaug'
if concept_type == 'memorization':
command += f' --use_8bit_adam --with_prior_preservation --prior_loss_weight=1.0 --mem_impath {mem_impath}'
with open(f'{save_path}/train.sh', 'w') as f:
command_s = ' '.join(command.split())
f.write(command_s)
res = subprocess.run(shlex.split(command))
if res.returncode == 0:
result_message = 'Training Completed!'
else:
result_message = 'Training Failed!'
weight_paths = sorted(Path(save_path).glob('*.bin'))
print(weight_paths)
return gr.update(value=result_message), weight_paths[0]
def inference(model_path, prompt, n_steps, generator):
import sys
sys.path.append('concept-ablation/diffusers/.')
from model_pipeline import CustomDiffusionPipeline
import torch
pipe = CustomDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4", torch_dtype=torch.float16).to("cuda")
image1 = pipe(prompt, num_inference_steps=n_steps, guidance_scale=6., eta=1., generator=generator).images[0]
pipe.load_model(model_path)
image2 = pipe(prompt, num_inference_steps=n_steps, guidance_scale=6., eta=1., generator=generator).images[0]
return image1, image2