Spaces:
Sleeping
Sleeping
File size: 5,130 Bytes
8173ae1 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 |
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 |