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