File size: 6,119 Bytes
8b7a3d1
 
 
 
 
 
 
 
db0cd98
8b7a3d1
 
 
 
 
 
a3f0757
8b7a3d1
 
 
 
 
c0a7c3c
 
 
 
 
8b7a3d1
a3f0757
8b7a3d1
a3f0757
 
8b7a3d1
a3f0757
8b7a3d1
07a02e5
 
 
 
a3f0757
 
 
 
07a02e5
 
 
 
 
a3f0757
07a02e5
 
 
a3f0757
 
 
 
 
 
 
 
 
 
 
 
 
07a02e5
 
 
 
 
 
 
 
a3f0757
 
 
 
 
07a02e5
a3f0757
 
 
 
 
 
07a02e5
 
 
 
a3f0757
 
 
07a02e5
 
 
a3f0757
 
 
8b7a3d1
 
 
c0a7c3c
07a02e5
8b7a3d1
 
 
 
 
 
07a02e5
8b7a3d1
 
 
 
 
 
07a02e5
 
 
 
 
 
 
 
 
 
 
 
8b7a3d1
 
 
 
 
 
 
db0cd98
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
#!/usr/bin/env python

from __future__ import annotations

import os

import gradio as gr

from constants import UploadTarget
from inference import InferencePipeline
from trainer import Trainer


def create_training_demo(trainer: Trainer,
                         pipe: InferencePipeline | None = None) -> gr.Blocks:
    hf_token = os.getenv('HF_TOKEN')
    with gr.Blocks() as demo:
        with gr.Row():
            with gr.Column():
                with gr.Box():
                    gr.Markdown('Training Data')
                    training_video = gr.File(label='Training video')
                    training_prompt = gr.Textbox(
                        label='Training prompt',
                        max_lines=1,
                        placeholder='A man is surfing')
                    gr.Markdown('''
                        - Upload a video and write a `Training Prompt` that describes the video.
                        ''')

            with gr.Column():
                with gr.Box():
                    gr.Markdown('Training Parameters')
                    with gr.Row():
                        base_model = gr.Text(
                            label='Base Model',
                            value='CompVis/stable-diffusion-v1-4',
                            max_lines=1)
                        resolution = gr.Dropdown(choices=['512', '768'],
                                                 value='512',
                                                 label='Resolution',
                                                 visible=False)

                    input_token = gr.Text(label='Hugging Face Write Token',
                                          placeholder='',
                                          visible=False if hf_token else True)
                    with gr.Accordion('Advanced settings', open=False):
                        num_training_steps = gr.Number(
                            label='Number of Training Steps',
                            value=300,
                            precision=0)
                        learning_rate = gr.Number(label='Learning Rate',
                                                  value=0.000035)
                        gradient_accumulation = gr.Number(
                            label='Number of Gradient Accumulation',
                            value=1,
                            precision=0)
                        seed = gr.Slider(label='Seed',
                                         minimum=0,
                                         maximum=100000,
                                         step=1,
                                         randomize=True,
                                         value=0)
                        fp16 = gr.Checkbox(label='FP16', value=True)
                        use_8bit_adam = gr.Checkbox(label='Use 8bit Adam',
                                                    value=False)
                        checkpointing_steps = gr.Number(
                            label='Checkpointing Steps',
                            value=1000,
                            precision=0)
                        validation_epochs = gr.Number(
                            label='Validation Epochs', value=100, precision=0)
                    gr.Markdown('''
                        - The base model must be a Stable Diffusion model compatible with [diffusers](https://github.com/huggingface/diffusers) library.
                        - Expected time to train a model for 300 steps: ~20 minutes with T4
                        - You can check the training status by pressing the "Open logs" button if you are running this on your Space.
                        ''')

        with gr.Row():
            with gr.Column():
                gr.Markdown('Output Model')
                output_model_name = gr.Text(label='Name of your model',
                                            placeholder='The surfer man',
                                            max_lines=1)
                validation_prompt = gr.Text(
                    label='Validation Prompt',
                    placeholder=
                    'prompt to test the model, e.g: a dog is surfing')
            with gr.Column():
                gr.Markdown('Upload Settings')
                with gr.Row():
                    upload_to_hub = gr.Checkbox(label='Upload model to Hub',
                                                value=True)
                    use_private_repo = gr.Checkbox(label='Private', value=True)
                    delete_existing_repo = gr.Checkbox(
                        label='Delete existing repo of the same name',
                        value=False)
                    upload_to = gr.Radio(
                        label='Upload to',
                        choices=[_.value for _ in UploadTarget],
                        value=UploadTarget.MODEL_LIBRARY.value)

        remove_gpu_after_training = gr.Checkbox(
            label='Remove GPU after training',
            value=False,
            interactive=bool(os.getenv('SPACE_ID')),
            visible=False)
        run_button = gr.Button('Start Training')

        with gr.Box():
            gr.Markdown('Output message')
            output_message = gr.Markdown()

        if pipe is not None:
            run_button.click(fn=pipe.clear)
        run_button.click(
            fn=trainer.run,
            inputs=[
                training_video, training_prompt, output_model_name,
                delete_existing_repo, validation_prompt, base_model,
                resolution, num_training_steps, learning_rate,
                gradient_accumulation, seed, fp16, use_8bit_adam,
                checkpointing_steps, validation_epochs, upload_to_hub,
                use_private_repo, delete_existing_repo, upload_to,
                remove_gpu_after_training, input_token
            ],
            outputs=output_message)
    return demo


if __name__ == '__main__':
    hf_token = os.getenv('HF_TOKEN')
    trainer = Trainer(hf_token)
    demo = create_training_demo(trainer)
    demo.queue(api_open=False, max_size=1).launch()