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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 |